Digex Qualtrics Survey Responses

Dataset statistics

Number of variables	49
Number of observations	499
Missing cells	4729
Missing cells (%)	19.3%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	191.1 KiB
Average record size in memory	392.3 B

Variable types

Numeric	11
Categorical	38

Alerts

`sm_aware` has a high cardinality: 65 distinct values	High cardinality
`sm_data_use` has a high cardinality: 162 distinct values	High cardinality
`ethic_appr` has a high cardinality: 498 distinct values	High cardinality
`study_1_conc` has a high cardinality: 270 distinct values	High cardinality
`study_1_add_info` has a high cardinality: 107 distinct values	High cardinality
`study_2_conc` has a high cardinality: 283 distinct values	High cardinality
`study_2_add_info` has a high cardinality: 109 distinct values	High cardinality
`study_3_conc` has a high cardinality: 247 distinct values	High cardinality
`study_3_add_info` has a high cardinality: 92 distinct values	High cardinality
`study_4_conc` has a high cardinality: 267 distinct values	High cardinality
`study_4_add_info` has a high cardinality: 104 distinct values	High cardinality
`design_add_fac` has a high cardinality: 261 distinct values	High cardinality
`rank_add_fac_1` has a high cardinality: 118 distinct values	High cardinality
`lat` is highly correlated with `long` and 3 other fields	High correlation
`long` is highly correlated with `lat` and 3 other fields	High correlation
`study_3_add_info` is highly correlated with `lat` and 18 other fields	High correlation
`rank_add_fac_1_pos` is highly correlated with `sm_aware` and 3 other fields	High correlation
`rank_add_fac_2` is highly correlated with `lat` and 17 other fields	High correlation
`rank_add_fac_2_pos` is highly correlated with `politic_pref` and 7 other fields	High correlation
`rank_add_fac_3` is highly correlated with `lat` and 14 other fields	High correlation
`rank_add_fac_3_pos` is highly correlated with `politic_pref` and 6 other fields	High correlation
`sm_use` is highly correlated with `study_3_add_info` and 1 other fields	High correlation
`age` is highly correlated with `study_3_add_info` and 1 other fields	High correlation
`gender_id` is highly correlated with `ethnic_id` and 3 other fields	High correlation
`ethnic_id` is highly correlated with `gender_id` and 1 other fields	High correlation
`edu` is highly correlated with `study_3_add_info`	High correlation
`politic_pref` is highly correlated with `ethnic_id` and 3 other fields	High correlation
`sm_aware` is highly correlated with `sm_expmt_inerct` and 3 other fields	High correlation
`sm_expmt_inerct` is highly correlated with `sm_aware` and 2 other fields	High correlation
`study_1_ethic_acc` is highly correlated with `study_2_ethic_acc` and 4 other fields	High correlation
`study_2_ethic_acc` is highly correlated with `study_1_ethic_acc` and 2 other fields	High correlation
`study_3_ethic_acc` is highly correlated with `rank_add_fac_2` and 1 other fields	High correlation
`study_4_ethic_acc` is highly correlated with `study_1_ethic_acc` and 2 other fields	High correlation
`design_cont` is highly correlated with `study_3_add_info` and 8 other fields	High correlation
`design_num_users` is highly correlated with `study_3_add_info` and 6 other fields	High correlation
`design_res_purp` is highly correlated with `study_3_add_info` and 8 other fields	High correlation
`design_len_data` is highly correlated with `study_3_add_info` and 6 other fields	High correlation
`design_admin_inter` is highly correlated with `study_3_add_info` and 7 other fields	High correlation
`design_inter_type` is highly correlated with `study_3_add_info` and 4 other fields	High correlation
`design_partic_aware` is highly correlated with `study_1_ethic_acc` and 3 other fields	High correlation
`design_inter_impact` is highly correlated with `study_3_add_info` and 7 other fields	High correlation
`design_type_data` is highly correlated with `study_3_add_info` and 9 other fields	High correlation
`rank_sci_repro` is highly correlated with `rank_just` and 1 other fields	High correlation
`rank_resp` is highly correlated with `rank_harms` and 2 other fields	High correlation
`rank_just` is highly correlated with `rank_sci_repro` and 1 other fields	High correlation
`rank_anony` is highly correlated with `rank_add_fac_2_pos`	High correlation
`rank_harms` is highly correlated with `study_3_add_info` and 4 other fields	High correlation
`rank_balance` is highly correlated with `rank_sci_repro` and 3 other fields	High correlation
`study_1_conc` has 204 (40.9%) missing values	Missing
`study_1_add_info` has 353 (70.7%) missing values	Missing
`study_2_conc` has 191 (38.3%) missing values	Missing
`study_2_add_info` has 355 (71.1%) missing values	Missing
`study_3_conc` has 225 (45.1%) missing values	Missing
`study_3_add_info` has 371 (74.3%) missing values	Missing
`study_4_conc` has 203 (40.7%) missing values	Missing
`study_4_add_info` has 355 (71.1%) missing values	Missing
`design_add_fac` has 103 (20.6%) missing values	Missing
`rank_add_fac_1` has 351 (70.3%) missing values	Missing
`rank_add_fac_1_pos` has 342 (68.5%) missing values	Missing
`rank_add_fac_2` has 431 (86.4%) missing values	Missing
`rank_add_fac_2_pos` has 402 (80.6%) missing values	Missing
`rank_add_fac_3` has 435 (87.2%) missing values	Missing
`rank_add_fac_3_pos` has 408 (81.8%) missing values	Missing
`df_index` is uniformly distributed	Uniform
`ethic_appr` is uniformly distributed	Uniform
`df_index` has unique values	Unique

Reproduction

Analysis started	2022-11-16 16:17:50.519771
Analysis finished	2022-11-16 16:18:11.658829
Duration	21.14 seconds
Software version	pandas-profiling v3.4.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

UNIFORM
UNIQUE

Distinct	499
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	250

Minimum	1
Maximum	499
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	25.9
Q1	125.5
median	250
Q3	374.5
95-th percentile	474.1
Maximum	499
Range	498
Interquartile range (IQR)	249

Descriptive statistics

Standard deviation	144.1931575
Coefficient of variation (CV)	0.57677263
Kurtosis	-1.2
Mean	250
Median Absolute Deviation (MAD)	125
Skewness	0
Sum	124750
Variance	20791.66667
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.2%
329	1	0.2%
342	1	0.2%
341	1	0.2%
340	1	0.2%
339	1	0.2%
338	1	0.2%
337	1	0.2%
336	1	0.2%
335	1	0.2%
Other values (489)	489	98.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%

Value	Count	Frequency (%)
499	1	0.2%
498	1	0.2%
497	1	0.2%
496	1	0.2%
495	1	0.2%
494	1	0.2%
493	1	0.2%
492	1	0.2%
491	1	0.2%
490	1	0.2%

lat
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	468
Distinct (%)	93.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	37.4252477

Minimum	25.4572
Maximum	47.8978
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	25.4572
5-th percentile	28.03196
Q1	33.8946
median	38.9507
Q3	41.1971
95-th percentile	44.26215
Maximum	47.8978
Range	22.4406
Interquartile range (IQR)	7.3025

Descriptive statistics

Standard deviation	5.144001204
Coefficient of variation (CV)	0.1374473523
Kurtosis	-0.5901140424
Mean	37.4252477
Median Absolute Deviation (MAD)	3.411
Skewness	-0.5037155839
Sum	18675.1986
Variance	26.46074839
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
37.751	4	0.8%
34.8965	3	0.6%
36.1671	3	0.6%
34.0007	2	0.4%
40.8275	2	0.4%
42.2314	2	0.4%
39.0805	2	0.4%
26.1481	2	0.4%
40.7035	2	0.4%
40.3226	2	0.4%
Other values (458)	475	95.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
25.4572	1	0.2%
25.5333	1	0.2%
25.6639	1	0.2%
25.6666	1	0.2%
25.7738	1	0.2%
25.8119	1	0.2%
26.1481	2	0.4%
26.1858	1	0.2%
26.2134	1	0.2%
26.5367	1	0.2%

Value	Count	Frequency (%)
47.8978	1	0.2%
47.8977	1	0.2%
47.6901	1	0.2%
47.6631	1	0.2%
47.6034	1	0.2%
47.4221	1	0.2%
47.1173	1	0.2%
46.8393	1	0.2%
46.4604	1	0.2%
46.1548	1	0.2%

long
Real number (ℝ)

HIGH CORRELATION

Distinct	469
Distinct (%)	94.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-86.3084996

Minimum	-123.0592
Maximum	-70.3899
Zeros	0
Zeros (%)	0.0%
Negative	499
Negative (%)	100.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	-123.0592
5-th percentile	-118.12652
Q1	-90.0043
median	-83.1895
Q3	-78.54575
95-th percentile	-73.07752
Maximum	-70.3899
Range	52.6693
Interquartile range (IQR)	11.45855

Descriptive statistics

Standard deviation	12.17469623
Coefficient of variation (CV)	-0.1410602233
Kurtosis	1.777511773
Mean	-86.3084996
Median Absolute Deviation (MAD)	5.7951
Skewness	-1.450253531
Sum	-43067.9413
Variance	148.2232283
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
-97.822	4	0.8%
-76.8869	3	0.6%
-86.7861	3	0.6%
-85.7098	2	0.4%
-73.1225	2	0.4%
-84.4559	2	0.4%
-80.2088	2	0.4%
-73.9235	2	0.4%
-76.4042	2	0.4%
-81.0348	2	0.4%
Other values (459)	475	95.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
-123.0592	1	0.2%
-123.0461	1	0.2%
-122.8865	1	0.2%
-122.6417	1	0.2%
-122.5091	1	0.2%
-122.3747	1	0.2%
-122.3414	1	0.2%
-122.3029	1	0.2%
-122.0693	1	0.2%
-122.0182	1	0.2%

Value	Count	Frequency (%)
-70.3899	1	0.2%
-70.4914	1	0.2%
-70.5627	1	0.2%
-70.8499	1	0.2%
-70.9465	1	0.2%
-70.95	2	0.4%
-71.054	1	0.2%
-71.0714	1	0.2%
-71.0951	1	0.2%
-71.1836	1	0.2%

sm_use
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Facebook	258
Reddit	133
Twitter	108

Length

Max length	8
Median length	8
Mean length	7.250501002
Min length	6

Characters and Unicode

Total characters	3618
Distinct characters	14
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Facebook
2nd row	Twitter
3rd row	Facebook
4th row	Facebook
5th row	Twitter

Common Values

Value	Count	Frequency (%)
Facebook	258	51.7%
Reddit	133	26.7%
Twitter	108	21.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
facebook	258	51.7%
reddit	133	26.7%
twitter	108	21.6%

Most occurring characters

Value	Count	Frequency (%)
o	516	14.3%
e	499	13.8%
t	349	9.6%
d	266	7.4%
F	258	7.1%
a	258	7.1%
c	258	7.1%
b	258	7.1%
k	258	7.1%
i	241	6.7%
Other values (4)	457	12.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3119	86.2%
Uppercase Letter	499	13.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	516	16.5%
e	499	16.0%
t	349	11.2%
d	266	8.5%
a	258	8.3%
c	258	8.3%
b	258	8.3%
k	258	8.3%
i	241	7.7%
w	108	3.5%

Uppercase Letter

Value	Count	Frequency (%)
F	258	51.7%
R	133	26.7%
T	108	21.6%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3618	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	516	14.3%
e	499	13.8%
t	349	9.6%
d	266	7.4%
F	258	7.1%
a	258	7.1%
c	258	7.1%
b	258	7.1%
k	258	7.1%
i	241	6.7%
Other values (4)	457	12.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3618	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	516	14.3%
e	499	13.8%
t	349	9.6%
d	266	7.4%
F	258	7.1%
a	258	7.1%
c	258	7.1%
b	258	7.1%
k	258	7.1%
i	241	6.7%
Other values (4)	457	12.6%

age
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	60
Distinct (%)	12.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	41.66332665

Minimum	18
Maximum	78
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	18
5-th percentile	23
Q1	31
median	39
Q3	51.5
95-th percentile	67
Maximum	78
Range	60
Interquartile range (IQR)	20.5

Descriptive statistics

Standard deviation	13.63593166
Coefficient of variation (CV)	0.3272885954
Kurtosis	-0.5585557113
Mean	41.66332665
Median Absolute Deviation (MAD)	10
Skewness	0.5655176939
Sum	20790
Variance	185.9386323
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
35	22	4.4%
34	20	4.0%
37	19	3.8%
29	18	3.6%
27	18	3.6%
26	17	3.4%
44	15	3.0%
31	15	3.0%
38	15	3.0%
23	14	2.8%
Other values (50)	326	65.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
18	1	0.2%
19	4	0.8%
20	3	0.6%
21	2	0.4%
22	2	0.4%
23	14	2.8%
24	8	1.6%
25	10	2.0%
26	17	3.4%
27	18	3.6%

Value	Count	Frequency (%)
78	1	0.2%
76	3	0.6%
75	1	0.2%
74	1	0.2%
73	2	0.4%
72	1	0.2%
71	2	0.4%
70	6	1.2%
69	3	0.6%
68	2	0.4%

gender_id
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Male	282
Female	207
Non-binary / third gender	8
Prefer not to say	2

Length

Max length	25
Median length	4
Mean length	5.218436874
Min length	4

Characters and Unicode

Total characters	2604
Distinct characters	23
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Male
2nd row	Male
3rd row	Female
4th row	Female
5th row	Female

Common Values

Value	Count	Frequency (%)
Male	282	56.5%
Female	207	41.5%
Non-binary / third gender	8	1.6%
Prefer not to say	2	0.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
male	282	53.3%
female	207	39.1%
non-binary	8	1.5%
	8	1.5%
third	8	1.5%
gender	8	1.5%
prefer	2	0.4%
not	2	0.4%
to	2	0.4%
say	2	0.4%

Most occurring characters

Value	Count	Frequency (%)
e	716	27.5%
a	499	19.2%
l	489	18.8%
M	282	10.8%
F	207	7.9%
m	207	7.9%
	30	1.2%
r	28	1.1%
n	26	1.0%
d	16	0.6%
Other values (13)	104	4.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2059	79.1%
Uppercase Letter	499	19.2%
Space Separator	30	1.2%
Dash Punctuation	8	0.3%
Other Punctuation	8	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	716	34.8%
a	499	24.2%
l	489	23.7%
m	207	10.1%
r	28	1.4%
n	26	1.3%
d	16	0.8%
i	16	0.8%
o	12	0.6%
t	12	0.6%
Other values (6)	38	1.8%

Uppercase Letter

Value	Count	Frequency (%)
M	282	56.5%
F	207	41.5%
N	8	1.6%
P	2	0.4%

Space Separator

Value	Count	Frequency (%)
	30	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	8	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	8	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2558	98.2%
Common	46	1.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	716	28.0%
a	499	19.5%
l	489	19.1%
M	282	11.0%
F	207	8.1%
m	207	8.1%
r	28	1.1%
n	26	1.0%
d	16	0.6%
i	16	0.6%
Other values (10)	72	2.8%

Common

Value	Count	Frequency (%)
	30	65.2%
-	8	17.4%
/	8	17.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2604	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	716	27.5%
a	499	19.2%
l	489	18.8%
M	282	10.8%
F	207	7.9%
m	207	7.9%
	30	1.2%
r	28	1.1%
n	26	1.0%
d	16	0.6%
Other values (13)	104	4.0%

ethnic_id
Categorical

HIGH CORRELATION

Distinct	12
Distinct (%)	2.4%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

White / Caucasian	397
African-American	32
Mixed race	20
Hispanic	19
Asian - Eastern	16
Other values (7)	15

Length

Max length	17
Median length	17
Mean length	16.15230461
Min length	5

Characters and Unicode

Total characters	8060
Distinct characters	34
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	1.0%

Sample

1st row	Asian - Eastern
2nd row	Mixed race
3rd row	Pacific Islander
4th row	White / Caucasian
5th row	Native-American

Common Values

Value	Count	Frequency (%)
White / Caucasian	397	79.6%
African-American	32	6.4%
Mixed race	20	4.0%
Hispanic	19	3.8%
Asian - Eastern	16	3.2%
Asian - Indian	7	1.4%
Native-American	3	0.6%
Pacific Islander	1	0.2%
Prefer not to say	1	0.2%
Asian - Southeast	1	0.2%
Other values (2)	2	0.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	421	30.8%
white	397	29.1%
caucasian	397	29.1%
african-american	32	2.3%
asian	24	1.8%
mixed	20	1.5%
race	20	1.5%
hispanic	19	1.4%
eastern	16	1.2%
indian	7	0.5%
Other values (10)	12	0.9%

Most occurring characters

Value	Count	Frequency (%)
a	1353	16.8%
i	956	11.9%
	866	10.7%
n	540	6.7%
c	505	6.3%
e	497	6.2%
s	459	5.7%
t	421	5.2%
h	399	5.0%
C	398	4.9%
Other values (24)	1666	20.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5782	71.7%
Uppercase Letter	956	11.9%
Space Separator	866	10.7%
Other Punctuation	397	4.9%
Dash Punctuation	59	0.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	1353	23.4%
i	956	16.5%
n	540	9.3%
c	505	8.7%
e	497	8.6%
s	459	7.9%
t	421	7.3%
h	399	6.9%
u	398	6.9%
r	109	1.9%
Other values (10)	145	2.5%

Uppercase Letter

Value	Count	Frequency (%)
C	398	41.6%
W	397	41.5%
A	91	9.5%
M	20	2.1%
H	19	2.0%
E	16	1.7%
I	8	0.8%
N	3	0.3%
P	2	0.2%
S	1	0.1%

Space Separator

Value	Count	Frequency (%)
	866	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	397	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	59	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6738	83.6%
Common	1322	16.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	1353	20.1%
i	956	14.2%
n	540	8.0%
c	505	7.5%
e	497	7.4%
s	459	6.8%
t	421	6.2%
h	399	5.9%
C	398	5.9%
u	398	5.9%
Other values (21)	812	12.1%

Common

Value	Count	Frequency (%)
	866	65.5%
/	397	30.0%
-	59	4.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8060	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	1353	16.8%
i	956	11.9%
	866	10.7%
n	540	6.7%
c	505	6.3%
e	497	6.2%
s	459	5.7%
t	421	5.2%
h	399	5.0%
C	398	4.9%
Other values (24)	1666	20.7%

edu
Categorical

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Bachelor's degree	222
Highschool	153
Master's degree or above	87
Associate's degree	22
Some college	7
Other values (2)	8

Length

Max length	24
Median length	19
Mean length	16.06412826
Min length	10

Characters and Unicode

Total characters	8016
Distinct characters	27
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Highschool
2nd row	Highschool
3rd row	Bachelor's degree
4th row	Highschool
5th row	Highschool

Common Values

Value	Count	Frequency (%)
Bachelor's degree	222	44.5%
Highschool	153	30.7%
Master's degree or above	87	17.4%
Associate's degree	22	4.4%
Some college	7	1.4%
Prefer not to say	4	0.8%
Vocational training	4	0.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
degree	331	32.2%
bachelor's	222	21.6%
highschool	153	14.9%
master's	87	8.5%
or	87	8.5%
above	87	8.5%
associate's	22	2.1%
some	7	0.7%
college	7	0.7%
prefer	4	0.4%
Other values (5)	20	1.9%

Most occurring characters

Value	Count	Frequency (%)
e	1440	18.0%
o	754	9.4%
r	739	9.2%
s	619	7.7%
h	528	6.6%
	528	6.6%
g	495	6.2%
a	434	5.4%
c	408	5.1%
l	393	4.9%
Other values (17)	1678	20.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6658	83.1%
Space Separator	528	6.6%
Uppercase Letter	499	6.2%
Other Punctuation	331	4.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1440	21.6%
o	754	11.3%
r	739	11.1%
s	619	9.3%
h	528	7.9%
g	495	7.4%
a	434	6.5%
c	408	6.1%
l	393	5.9%
d	331	5.0%
Other values (8)	517	7.8%

Uppercase Letter

Value	Count	Frequency (%)
B	222	44.5%
H	153	30.7%
M	87	17.4%
A	22	4.4%
S	7	1.4%
P	4	0.8%
V	4	0.8%

Space Separator

Value	Count	Frequency (%)
	528	100.0%

Other Punctuation

Value	Count	Frequency (%)
'	331	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7157	89.3%
Common	859	10.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1440	20.1%
o	754	10.5%
r	739	10.3%
s	619	8.6%
h	528	7.4%
g	495	6.9%
a	434	6.1%
c	408	5.7%
l	393	5.5%
d	331	4.6%
Other values (15)	1016	14.2%

Common

Value	Count	Frequency (%)
	528	61.5%
'	331	38.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8016	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1440	18.0%
o	754	9.4%
r	739	9.2%
s	619	7.7%
h	528	6.6%
	528	6.6%
g	495	6.2%
a	434	5.4%
c	408	5.1%
l	393	4.9%
Other values (17)	1678	20.9%

politic_pref
Categorical

HIGH CORRELATION

Distinct	6
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Very liberal	150
Slightly liberal	126
Slightly conservative	96
Neutral/ Neither conservative or liberal	89
Very conservative	35

Length

Max length	40
Median length	21
Mean length	20.11623246
Min length	12

Characters and Unicode

Total characters	10038
Distinct characters	23
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Slightly liberal
2nd row	Neutral/ Neither conservative or liberal
3rd row	Very liberal
4th row	Slightly conservative
5th row	Very liberal

Common Values

Value	Count	Frequency (%)
Very liberal	150	30.1%
Slightly liberal	126	25.3%
Slightly conservative	96	19.2%
Neutral/ Neither conservative or liberal	89	17.8%
Very conservative	35	7.0%
Prefer not to say	3	0.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
liberal	365	28.7%
slightly	222	17.5%
conservative	220	17.3%
very	185	14.6%
neutral	89	7.0%
neither	89	7.0%
or	89	7.0%
prefer	3	0.2%
not	3	0.2%
to	3	0.2%

Most occurring characters

Value	Count	Frequency (%)
e	1263	12.6%
l	1263	12.6%
r	1043	10.4%
i	896	8.9%
	772	7.7%
a	677	6.7%
t	626	6.2%
v	440	4.4%
y	410	4.1%
b	365	3.6%
Other values (13)	2283	22.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8589	85.6%
Space Separator	772	7.7%
Uppercase Letter	588	5.9%
Other Punctuation	89	0.9%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1263	14.7%
l	1263	14.7%
r	1043	12.1%
i	896	10.4%
a	677	7.9%
t	626	7.3%
v	440	5.1%
y	410	4.8%
b	365	4.2%
o	315	3.7%
Other values (7)	1291	15.0%

Uppercase Letter

Value	Count	Frequency (%)
S	222	37.8%
V	185	31.5%
N	178	30.3%
P	3	0.5%

Space Separator

Value	Count	Frequency (%)
	772	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	89	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9177	91.4%
Common	861	8.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1263	13.8%
l	1263	13.8%
r	1043	11.4%
i	896	9.8%
a	677	7.4%
t	626	6.8%
v	440	4.8%
y	410	4.5%
b	365	4.0%
o	315	3.4%
Other values (11)	1879	20.5%

Common

Value	Count	Frequency (%)
	772	89.7%
/	89	10.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	10038	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1263	12.6%
l	1263	12.6%
r	1043	10.4%
i	896	8.9%
	772	7.7%
a	677	6.7%
t	626	6.2%
v	440	4.4%
y	410	4.1%
b	365	3.6%
Other values (13)	2283	22.7%

sm_res_purp
Categorical

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Moderately aware	128
Very aware	119
Slightly aware	117
Not at all aware	76
Extremely aware	59

Length

Max length	16
Median length	15
Mean length	13.98196393
Min length	10

Characters and Unicode

Total characters	6977
Distinct characters	20
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Extremely aware
2nd row	Moderately aware
3rd row	Extremely aware
4th row	Moderately aware
5th row	Extremely aware

Common Values

Value	Count	Frequency (%)
Moderately aware	128	25.7%
Very aware	119	23.8%
Slightly aware	117	23.4%
Not at all aware	76	15.2%
Extremely aware	59	11.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
aware	499	43.4%
moderately	128	11.1%
very	119	10.3%
slightly	117	10.2%
not	76	6.6%
at	76	6.6%
all	76	6.6%
extremely	59	5.1%

Most occurring characters

Value	Count	Frequency (%)
a	1278	18.3%
e	992	14.2%
r	805	11.5%
	651	9.3%
l	573	8.2%
w	499	7.2%
t	456	6.5%
y	423	6.1%
o	204	2.9%
M	128	1.8%
Other values (10)	968	13.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5827	83.5%
Space Separator	651	9.3%
Uppercase Letter	499	7.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	1278	21.9%
e	992	17.0%
r	805	13.8%
l	573	9.8%
w	499	8.6%
t	456	7.8%
y	423	7.3%
o	204	3.5%
d	128	2.2%
i	117	2.0%
Other values (4)	352	6.0%

Uppercase Letter

Value	Count	Frequency (%)
M	128	25.7%
V	119	23.8%
S	117	23.4%
N	76	15.2%
E	59	11.8%

Space Separator

Value	Count	Frequency (%)
	651	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6326	90.7%
Common	651	9.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	1278	20.2%
e	992	15.7%
r	805	12.7%
l	573	9.1%
w	499	7.9%
t	456	7.2%
y	423	6.7%
o	204	3.2%
M	128	2.0%
d	128	2.0%
Other values (9)	840	13.3%

Common

Value	Count	Frequency (%)
	651	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6977	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	1278	18.3%
e	992	14.2%
r	805	11.5%
	651	9.3%
l	573	8.2%
w	499	7.2%
t	456	6.5%
y	423	6.1%
o	204	2.9%
M	128	1.8%
Other values (10)	968	13.9%

sm_aware
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct	65
Distinct (%)	13.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	154
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	47
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are readily accessible to researchers and easy to collect	32
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… are readily accessible to researchers and easy to collect	31
… are large and can contain millions of data points,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	18
Other values (60)	217

Length

Max length	547
Median length	435
Mean length	275.8496994
Min length	17

Characters and Unicode

Total characters	137649
Distinct characters	32
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	26 ?
Unique (%)	5.2%

Sample

1st row	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys)
2nd row	… are large and can contain millions of data points
3rd row	… are large and can contain millions of data points,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect
4th row	… are large and can contain millions of data points,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect
5th row	… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect

Common Values

Value	Count	Frequency (%)
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	154	30.9%
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	47	9.4%
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are readily accessible to researchers and easy to collect	32	6.4%
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… are readily accessible to researchers and easy to collect	31	6.2%
… are large and can contain millions of data points,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	18	3.6%
… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys)	17	3.4%
… are large and can contain millions of data points,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	17	3.4%
… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	14	2.8%
… are large and can contain millions of data points,… are readily accessible to researchers and easy to collect	12	2.4%
… are readily accessible to researchers and easy to collect	10	2.0%
Other values (55)	147	29.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
and	1188	5.8%
are	1165	5.6%
to	1122	5.4%
can	764	3.7%
researchers	709	3.4%
in	667	3.2%
not	645	3.1%
…	492	2.4%
of	432	2.1%
accessible	413	2.0%
Other values (62)	13040	63.2%

Most occurring characters

Value	Count	Frequency (%)
	20138	14.6%
e	14990	10.9%
t	10814	7.9%
a	10523	7.6%
r	9133	6.6%
n	8728	6.3%
i	8217	6.0%
o	8153	5.9%
s	7674	5.6%
c	6935	5.0%
Other values (22)	32344	23.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	112427	81.7%
Space Separator	20138	14.6%
Other Punctuation	3976	2.9%
Dash Punctuation	371	0.3%
Open Punctuation	349	0.3%
Close Punctuation	349	0.3%
Final Punctuation	32	< 0.1%
Uppercase Letter	7	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	14990	13.3%
t	10814	9.6%
a	10523	9.4%
r	9133	8.1%
n	8728	7.8%
i	8217	7.3%
o	8153	7.3%
s	7674	6.8%
c	6935	6.2%
l	6779	6.0%
Other values (13)	20481	18.2%

Other Punctuation

Value	Count	Frequency (%)
…	1885	47.4%
,	1393	35.0%
.	698	17.6%

Space Separator

Value	Count	Frequency (%)
	20138	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	371	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	349	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	349	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	32	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	7	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	112434	81.7%
Common	25215	18.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	14990	13.3%
t	10814	9.6%
a	10523	9.4%
r	9133	8.1%
n	8728	7.8%
i	8217	7.3%
o	8153	7.3%
s	7674	6.8%
c	6935	6.2%
l	6779	6.0%
Other values (14)	20488	18.2%

Common

Value	Count	Frequency (%)
	20138	79.9%
…	1885	7.5%
,	1393	5.5%
.	698	2.8%
-	371	1.5%
(	349	1.4%
)	349	1.4%
’	32	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	135732	98.6%
Punctuation	1917	1.4%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	20138	14.8%
e	14990	11.0%
t	10814	8.0%
a	10523	7.8%
r	9133	6.7%
n	8728	6.4%
i	8217	6.1%
o	8153	6.0%
s	7674	5.7%
c	6935	5.1%
Other values (20)	30427	22.4%

Punctuation

Value	Count	Frequency (%)
…	1885	98.3%
’	32	1.7%

sm_expmt_inerct
Categorical

HIGH CORRELATION

Distinct	22
Distinct (%)	4.4%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

None of the above	69
Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")	68
Creating fake accounts ("bots")	57
Privately messaging users,Creating fake accounts ("bots")	50
Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots"),Secretly changing the content of what users see	40
Other values (17)	215

Length

Max length	170
Median length	109
Mean length	66.62925852
Min length	17

Characters and Unicode

Total characters	33248
Distinct characters	32
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.2%

Sample

1st row	Creating fake accounts ("bots"),Secretly changing the content of what users see
2nd row	Privately messaging users,Publicly posting on users' profiles,Secretly changing the content of what users see
3rd row	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots"),Secretly changing the content of what users see
4th row	Creating fake accounts ("bots")
5th row	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")

Common Values

Value	Count	Frequency (%)
None of the above	69	13.8%
Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")	68	13.6%
Creating fake accounts ("bots")	57	11.4%
Privately messaging users,Creating fake accounts ("bots")	50	10.0%
Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots"),Secretly changing the content of what users see	40	8.0%
Creating fake accounts ("bots"),Secretly changing the content of what users see	37	7.4%
Privately messaging users,Publicly posting on users' profiles	33	6.6%
Privately messaging users	29	5.8%
Publicly posting on users' profiles,Creating fake accounts ("bots")	23	4.6%
Privately messaging users,Creating fake accounts ("bots"),Secretly changing the content of what users see	22	4.4%
Other values (12)	71	14.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
users	406	9.8%
accounts	348	8.4%
fake	331	8.0%
privately	258	6.2%
messaging	258	6.2%
the	215	5.2%
of	215	5.2%
posting	214	5.2%
on	214	5.2%
bots	198	4.8%
Other values (20)	1487	35.9%

Most occurring characters

Value	Count	Frequency (%)
	3645	11.0%
e	3110	9.4%
s	3039	9.1%
t	2298	6.9%
n	2052	6.2%
a	1904	5.7%
o	1837	5.5%
i	1669	5.0%
r	1584	4.8%
g	1370	4.1%
Other values (22)	10740	32.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	26477	79.6%
Space Separator	3645	11.0%
Other Punctuation	1429	4.3%
Uppercase Letter	1035	3.1%
Close Punctuation	331	1.0%
Open Punctuation	331	1.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3110	11.7%
s	3039	11.5%
t	2298	8.7%
n	2052	7.8%
a	1904	7.2%
o	1837	6.9%
i	1669	6.3%
r	1584	6.0%
g	1370	5.2%
c	1365	5.2%
Other values (11)	6249	23.6%

Uppercase Letter

Value	Count	Frequency (%)
P	472	45.6%
C	331	32.0%
S	146	14.1%
N	69	6.7%
H	17	1.6%

Other Punctuation

Value	Count	Frequency (%)
"	662	46.3%
,	536	37.5%
'	231	16.2%

Space Separator

Value	Count	Frequency (%)
	3645	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	331	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	331	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	27512	82.7%
Common	5736	17.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3110	11.3%
s	3039	11.0%
t	2298	8.4%
n	2052	7.5%
a	1904	6.9%
o	1837	6.7%
i	1669	6.1%
r	1584	5.8%
g	1370	5.0%
c	1365	5.0%
Other values (16)	7284	26.5%

Common

Value	Count	Frequency (%)
	3645	63.5%
"	662	11.5%
,	536	9.3%
)	331	5.8%
(	331	5.8%
'	231	4.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	33248	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	3645	11.0%
e	3110	9.4%
s	3039	9.1%
t	2298	6.9%
n	2052	6.2%
a	1904	5.7%
o	1837	5.5%
i	1669	5.0%
r	1584	4.8%
g	1370	4.1%
Other values (22)	10740	32.3%

sm_data_use
Categorical

HIGH CARDINALITY

Distinct	162
Distinct (%)	32.5%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	167
Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	24
Political elections (e.g. voting behavior),Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	10
Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	10
Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	9
Other values (157)	279

Length

Max length	345
Median length	295
Mean length	259.8757515
Min length	15

Characters and Unicode

Total characters	129678
Distinct characters	34
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	98 ?
Unique (%)	19.6%

Sample

1st row	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks
2nd row	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks
3rd row	Political elections (e.g. voting behavior),Presidential approval ratings,Communication (e.g. spread of opinions and hate-speech),News consumption (e.g. sharing of misinformation),Social networks
4th row	Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)
5th row	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks

Common Values

Value	Count	Frequency (%)
Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	167	33.5%
Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	24	4.8%
Political elections (e.g. voting behavior),Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	10	2.0%
Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	10	2.0%
Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	9	1.8%
Political elections (e.g. voting behavior),Presidential approval ratings,Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	9	1.8%
Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	7	1.4%
Political elections (e.g. voting behavior),Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	7	1.4%
Communication (e.g. spread of opinions and hate-speech),News consumption (e.g. sharing of misinformation),Social networks	6	1.2%
Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	5	1.0%
Other values (152)	245	49.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
e.g	1982	16.1%
of	1177	9.6%
spread	753	6.1%
and	729	5.9%
sharing	419	3.4%
consumption	419	3.4%
environment-related	412	3.3%
sentiment	412	3.3%
opinions	404	3.3%
political	398	3.2%
Other values (55)	5197	42.2%

Most occurring characters

Value	Count	Frequency (%)
	11803	9.1%
e	11790	9.1%
i	10844	8.4%
n	10671	8.2%
o	10085	7.8%
s	7814	6.0%
a	7674	5.9%
t	7150	5.5%
c	5740	4.4%
r	4872	3.8%
Other values (24)	41235	31.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	102739	79.2%
Space Separator	11803	9.1%
Other Punctuation	6748	5.2%
Uppercase Letter	3283	2.5%
Close Punctuation	1982	1.5%
Open Punctuation	1982	1.5%
Dash Punctuation	1141	0.9%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	11790	11.5%
i	10844	10.6%
n	10671	10.4%
o	10085	9.8%
s	7814	7.6%
a	7674	7.5%
t	7150	7.0%
c	5740	5.6%
r	4872	4.7%
l	4064	4.0%
Other values (11)	22035	21.4%

Uppercase Letter

Value	Count	Frequency (%)
P	1148	35.0%
N	424	12.9%
C	404	12.3%
S	371	11.3%
H	349	10.6%
W	325	9.9%
E	262	8.0%

Other Punctuation

Value	Count	Frequency (%)
.	3964	58.7%
,	2784	41.3%

Space Separator

Value	Count	Frequency (%)
	11803	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1982	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1982	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1141	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	106022	81.8%
Common	23656	18.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	11790	11.1%
i	10844	10.2%
n	10671	10.1%
o	10085	9.5%
s	7814	7.4%
a	7674	7.2%
t	7150	6.7%
c	5740	5.4%
r	4872	4.6%
l	4064	3.8%
Other values (18)	25318	23.9%

Common

Value	Count	Frequency (%)
	11803	49.9%
.	3964	16.8%
,	2784	11.8%
)	1982	8.4%
(	1982	8.4%
-	1141	4.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	129678	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	11803	9.1%
e	11790	9.1%
i	10844	8.4%
n	10671	8.2%
o	10085	7.8%
s	7814	6.0%
a	7674	5.9%
t	7150	5.5%
c	5740	4.4%
r	4872	3.8%
Other values (24)	41235	31.8%

ethic_appr
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	498
Distinct (%)	99.8%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Ethics approval is needed for any research that involves human participants; their tissue and /or data to ensure that the dignity, rights, safety and well-being of all participants are the primary consideration of the research project.	2
The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.	1
That they are going to use the information they receive appropriately. They are not going to manipulate and misuse what they gather.	1
It means that, in the opinion of the institution, the study and its methods are morally acceptable.	1
Ethical approval means getting clearance to obtain data from a research subject.	1
Other values (493)	493

Length

Max length	1026
Median length	207
Mean length	134.7935872
Min length	15

Characters and Unicode

Total characters	67262
Distinct characters	66
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	497 ?
Unique (%)	99.6%

Sample

1st row	The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.
2nd row	I think Ethical Approval means that the experiment is gathering data without harm or injury to people.
3rd row	Researchers focus on ethical standards towards those they gain data from. They need approval of their approach and receive methods.
4th row	I would think that using "ethical approval" means that the things others collect on social media sites would need to be honest and moral. Hopefully, there would be no under-handedness used in collecting information.
5th row	A set of rules of what to do and what to not do.

Common Values

Value	Count	Frequency (%)
Ethics approval is needed for any research that involves human participants; their tissue and /or data to ensure that the dignity, rights, safety and well-being of all participants are the primary consideration of the research project.	2	0.4%
The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.	1	0.2%
That they are going to use the information they receive appropriately. They are not going to manipulate and misuse what they gather.	1	0.2%
It means that, in the opinion of the institution, the study and its methods are morally acceptable.	1	0.2%
Ethical approval means getting clearance to obtain data from a research subject.	1	0.2%
It means receiving approval from an IRB or other institution that has oversight over study approval. They make sure the studies to not hamr their subjects.	1	0.2%
Ethical approval from the institution means they will act in a way that responsible and takes in to account the persons they are researching.	1	0.2%
Proof that the experiment is not done against people's wills and if people ask, all data will be deleted.	1	0.2%
There is (or should be ) oversight from someone in charge, and who is ethical.	1	0.2%
Morally correct.	1	0.2%
Other values (488)	488	97.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	736	6.5%
to	474	4.2%
that	434	3.8%
and	295	2.6%
is	249	2.2%
ethical	249	2.2%
of	227	2.0%
it	217	1.9%
they	212	1.9%
approval	201	1.8%
Other values (1400)	8064	71.0%

Most occurring characters

Value	Count	Frequency (%)
	11017	16.4%
e	6612	9.8%
t	6115	9.1%
a	4947	7.4%
i	4019	6.0%
o	3823	5.7%
n	3645	5.4%
s	3457	5.1%
r	3449	5.1%
h	3300	4.9%
Other values (56)	16878	25.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	54478	81.0%
Space Separator	11017	16.4%
Other Punctuation	966	1.4%
Uppercase Letter	705	1.0%
Dash Punctuation	36	0.1%
Open Punctuation	22	< 0.1%
Close Punctuation	22	< 0.1%
Final Punctuation	8	< 0.1%
Control	7	< 0.1%
Decimal Number	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	6612	12.1%
t	6115	11.2%
a	4947	9.1%
i	4019	7.4%
o	3823	7.0%
n	3645	6.7%
s	3457	6.3%
r	3449	6.3%
h	3300	6.1%
l	2057	3.8%
Other values (16)	13054	24.0%

Uppercase Letter

Value	Count	Frequency (%)
I	257	36.5%
T	144	20.4%
E	91	12.9%
A	46	6.5%
B	24	3.4%
R	23	3.3%
M	19	2.7%
W	15	2.1%
P	9	1.3%
S	9	1.3%
Other values (15)	68	9.6%

Other Punctuation

Value	Count	Frequency (%)
.	535	55.4%
,	234	24.2%
'	141	14.6%
"	28	2.9%
/	12	1.2%
?	10	1.0%
;	4	0.4%
:	2	0.2%

Space Separator

Value	Count	Frequency (%)
	11017	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	36	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	22	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	22	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	8	100.0%

Control

Value	Count	Frequency (%)
	7	100.0%

Decimal Number

Value	Count	Frequency (%)
3	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	55183	82.0%
Common	12079	18.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	6612	12.0%
t	6115	11.1%
a	4947	9.0%
i	4019	7.3%
o	3823	6.9%
n	3645	6.6%
s	3457	6.3%
r	3449	6.3%
h	3300	6.0%
l	2057	3.7%
Other values (41)	13759	24.9%

Common

Value	Count	Frequency (%)
	11017	91.2%
.	535	4.4%
,	234	1.9%
'	141	1.2%
-	36	0.3%
"	28	0.2%
(	22	0.2%
)	22	0.2%
/	12	0.1%
?	10	0.1%
Other values (5)	22	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	67254	> 99.9%
Punctuation	8	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	11017	16.4%
e	6612	9.8%
t	6115	9.1%
a	4947	7.4%
i	4019	6.0%
o	3823	5.7%
n	3645	5.4%
s	3457	5.1%
r	3449	5.1%
h	3300	4.9%
Other values (55)	16870	25.1%

Punctuation

Value	Count	Frequency (%)
’	8	100.0%

study_1_ethic_acc
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Completely acceptable	157
Somewhat acceptable	144
Somewhat unacceptable	81
Neutral	62
Completey unacceptable	55

Length

Max length	22
Median length	21
Mean length	18.79358717
Min length	7

Characters and Unicode

Total characters	9378
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Completely acceptable
3rd row	Completely acceptable
4th row	Neutral
5th row	Completely acceptable

Common Values

Value	Count	Frequency (%)
Completely acceptable	157	31.5%
Somewhat acceptable	144	28.9%
Somewhat unacceptable	81	16.2%
Neutral	62	12.4%
Completey unacceptable	55	11.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
acceptable	301	32.2%
somewhat	225	24.0%
completely	157	16.8%
unacceptable	136	14.5%
neutral	62	6.6%
completey	55	5.9%

Most occurring characters

Value	Count	Frequency (%)
e	1585	16.9%
a	1161	12.4%
t	936	10.0%
c	874	9.3%
l	868	9.3%
p	649	6.9%
m	437	4.7%
	437	4.7%
o	437	4.7%
b	437	4.7%
Other values (9)	1557	16.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8442	90.0%
Uppercase Letter	499	5.3%
Space Separator	437	4.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1585	18.8%
a	1161	13.8%
t	936	11.1%
c	874	10.4%
l	868	10.3%
p	649	7.7%
m	437	5.2%
o	437	5.2%
b	437	5.2%
w	225	2.7%
Other values (5)	833	9.9%

Uppercase Letter

Value	Count	Frequency (%)
S	225	45.1%
C	212	42.5%
N	62	12.4%

Space Separator

Value	Count	Frequency (%)
	437	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8941	95.3%
Common	437	4.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1585	17.7%
a	1161	13.0%
t	936	10.5%
c	874	9.8%
l	868	9.7%
p	649	7.3%
m	437	4.9%
o	437	4.9%
b	437	4.9%
w	225	2.5%
Other values (8)	1332	14.9%

Common

Value	Count	Frequency (%)
	437	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9378	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1585	16.9%
a	1161	12.4%
t	936	10.0%
c	874	9.3%
l	868	9.3%
p	649	6.9%
m	437	4.7%
	437	4.7%
o	437	4.7%
b	437	4.7%
Other values (9)	1557	16.6%

study_1_conc
Categorical

HIGH CARDINALITY
MISSING

Distinct	270
Distinct (%)	91.5%
Missing	204
Missing (%)	40.9%
Memory size	4.0 KiB

na	20
Na	7
Same as before, this is all public and anyone can do these things, so I have no issue with it.	1
No informed consent. Troll farms and the people that pay them should be illegal.	1
Again, I feel the users should be informed on the situation and what is occurring.	1
Other values (265)	265

Length

Max length	812
Median length	198
Mean length	117.9084746
Min length	2

Characters and Unicode

Total characters	34783
Distinct characters	69
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	268 ?
Unique (%)	90.8%

Sample

1st row	No concerns. I would have loved to partake in this study in terms of watching the results.
2nd row	I feel if people know they are being judged they will act, speak, or write differently than if they don't know they are being analyzed.
3rd row	na
4th row	Easy enough for an outside government to try copying such a study with the sole purpose of creating much more polarization, hate, etc. Not that it hasn't been tried and tested perhaps innumerable times by all types of foreign or domestic entities as far as we know. No actual study would have really been needed to know that using a type of marketing manipulation could alter the recipients mood/levels of concern/anxiety/hate/etc.
5th row	The participants were not Were of this research study being conducted. Therefore it is unethical

Common Values

Value	Count	Frequency (%)
na	20	4.0%
Na	7	1.4%
Same as before, this is all public and anyone can do these things, so I have no issue with it.	1	0.2%
No informed consent. Troll farms and the people that pay them should be illegal.	1	0.2%
Again, I feel the users should be informed on the situation and what is occurring.	1	0.2%
My main concern remains that individuals were not giving informed consent. But, with the understanding that the posts are in the public domain - not just shared to friends - perhaps that is not really an ethical issue? I'm a bit torn here.	1	0.2%
Even though users were not informed about being in this research, it ultimately was for a good cause and helpful in making progress towards less hate speech on social media.	1	0.2%
I feel its quit acceptable posting something that reduces the hate in general also since it helps one to rethink their post.	1	0.2%
In order for the study's results to be accurate users couldn't know that the researchers were running a study.	1	0.2%
Using people's data without consent for a study seems unethical.	1	0.2%
Other values (260)	260	52.1%
(Missing)	204	40.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	297	4.9%
to	158	2.6%
that	157	2.6%
i	154	2.5%
a	141	2.3%
of	134	2.2%
is	125	2.0%
they	121	2.0%
study	108	1.8%
not	106	1.7%
Other values (1071)	4617	75.5%

Most occurring characters

Value	Count	Frequency (%)
	5913	17.0%
e	3747	10.8%
t	3055	8.8%
a	2226	6.4%
i	1925	5.5%
o	1870	5.4%
s	1834	5.3%
n	1813	5.2%
h	1582	4.5%
r	1497	4.3%
Other values (59)	9321	26.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	27561	79.2%
Space Separator	5913	17.0%
Other Punctuation	727	2.1%
Uppercase Letter	524	1.5%
Control	14	< 0.1%
Dash Punctuation	13	< 0.1%
Decimal Number	9	< 0.1%
Close Punctuation	8	< 0.1%
Final Punctuation	7	< 0.1%
Open Punctuation	7	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3747	13.6%
t	3055	11.1%
a	2226	8.1%
i	1925	7.0%
o	1870	6.8%
s	1834	6.7%
n	1813	6.6%
h	1582	5.7%
r	1497	5.4%
d	988	3.6%
Other values (16)	7024	25.5%

Uppercase Letter

Value	Count	Frequency (%)
I	214	40.8%
T	91	17.4%
A	39	7.4%
P	28	5.3%
N	27	5.2%
S	17	3.2%
W	15	2.9%
M	12	2.3%
E	12	2.3%
H	11	2.1%
Other values (12)	58	11.1%

Other Punctuation

Value	Count	Frequency (%)
.	365	50.2%
,	161	22.1%
'	128	17.6%
"	38	5.2%
?	17	2.3%
/	9	1.2%
!	5	0.7%
:	3	0.4%
;	1	0.1%

Decimal Number

Value	Count	Frequency (%)
0	5	55.6%
2	2	22.2%
1	1	11.1%
4	1	11.1%

Close Punctuation

Value	Count	Frequency (%)
)	6	75.0%
]	2	25.0%

Open Punctuation

Value	Count	Frequency (%)
(	5	71.4%
[	2	28.6%

Space Separator

Value	Count	Frequency (%)
	5913	100.0%

Control

Value	Count	Frequency (%)
	14	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	13	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	7	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	28085	80.7%
Common	6698	19.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3747	13.3%
t	3055	10.9%
a	2226	7.9%
i	1925	6.9%
o	1870	6.7%
s	1834	6.5%
n	1813	6.5%
h	1582	5.6%
r	1497	5.3%
d	988	3.5%
Other values (38)	7548	26.9%

Common

Value	Count	Frequency (%)
	5913	88.3%
.	365	5.4%
,	161	2.4%
'	128	1.9%
"	38	0.6%
?	17	0.3%
	14	0.2%
-	13	0.2%
/	9	0.1%
’	7	0.1%
Other values (11)	33	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	34776	> 99.9%
Punctuation	7	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	5913	17.0%
e	3747	10.8%
t	3055	8.8%
a	2226	6.4%
i	1925	5.5%
o	1870	5.4%
s	1834	5.3%
n	1813	5.2%
h	1582	4.5%
r	1497	4.3%
Other values (58)	9314	26.8%

Punctuation

Value	Count	Frequency (%)
’	7	100.0%

study_1_add_info
Categorical

HIGH CARDINALITY
MISSING

Distinct	107
Distinct (%)	73.3%
Missing	353
Missing (%)	70.7%
Memory size	4.0 KiB

na	30
Na	8
no	2
N/a	2
NA.	2
Other values (102)	102

Length

Max length	316
Median length	161.5
Mean length	64.34246575
Min length	2

Characters and Unicode

Total characters	9394
Distinct characters	59
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	102 ?
Unique (%)	69.9%

Sample

1st row	na
2nd row	I would be interested to know what kind of messages they sent the hate speech users that got them to change their minds.
3rd row	Full disclosure of intent of researchers.
4th row	See comments from previous studies
5th row	Na

Common Values

Value	Count	Frequency (%)
na	30	6.0%
Na	8	1.6%
no	2	0.4%
N/a	2	0.4%
NA.	2	0.4%
The fact that the fake accounts were used to try and suppress hate speech, makes it more ethical in my opinion.	1	0.2%
I know that I certainly wouldn't like to be part of an experiment without me person consenting to it.	1	0.2%
I would love to see the message that was posted by the researches to what was posted in order to see what was said and how it was worded.	1	0.2%
No one is getting harmed or misinformed here. the study is actually trying to help people, so i think it's acceptable even though they did not know they were in a study.	1	0.2%
I would like to know more about what the replies actually said.	1	0.2%
Other values (97)	97	19.4%
(Missing)	353	70.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	94	5.6%
to	73	4.3%
i	46	2.7%
na	42	2.5%
it	35	2.1%
they	34	2.0%
of	32	1.9%
would	28	1.7%
were	27	1.6%
was	24	1.4%
Other values (489)	1250	74.2%

Most occurring characters

Value	Count	Frequency (%)
	1573	16.7%
e	1052	11.2%
t	808	8.6%
a	590	6.3%
o	562	6.0%
s	507	5.4%
n	467	5.0%
i	450	4.8%
h	414	4.4%
r	378	4.0%
Other values (49)	2593	27.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7429	79.1%
Space Separator	1573	16.7%
Other Punctuation	194	2.1%
Uppercase Letter	165	1.8%
Decimal Number	12	0.1%
Dash Punctuation	6	0.1%
Close Punctuation	6	0.1%
Open Punctuation	6	0.1%
Control	2	< 0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1052	14.2%
t	808	10.9%
a	590	7.9%
o	562	7.6%
s	507	6.8%
n	467	6.3%
i	450	6.1%
h	414	5.6%
r	378	5.1%
l	282	3.8%
Other values (16)	1919	25.8%

Uppercase Letter

Value	Count	Frequency (%)
I	70	42.4%
N	24	14.5%
T	13	7.9%
W	12	7.3%
A	10	6.1%
H	7	4.2%
S	5	3.0%
P	5	3.0%
M	3	1.8%
O	3	1.8%
Other values (7)	13	7.9%

Other Punctuation

Value	Count	Frequency (%)
.	105	54.1%
,	44	22.7%
'	24	12.4%
?	14	7.2%
/	4	2.1%
"	2	1.0%
%	1	0.5%

Decimal Number

Value	Count	Frequency (%)
0	8	66.7%
1	3	25.0%
4	1	8.3%

Space Separator

Value	Count	Frequency (%)
	1573	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	6	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	6	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	6	100.0%

Control

Value	Count	Frequency (%)
	2	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7594	80.8%
Common	1800	19.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1052	13.9%
t	808	10.6%
a	590	7.8%
o	562	7.4%
s	507	6.7%
n	467	6.1%
i	450	5.9%
h	414	5.5%
r	378	5.0%
l	282	3.7%
Other values (33)	2084	27.4%

Common

Value	Count	Frequency (%)
	1573	87.4%
.	105	5.8%
,	44	2.4%
'	24	1.3%
?	14	0.8%
0	8	0.4%
-	6	0.3%
)	6	0.3%
(	6	0.3%
/	4	0.2%
Other values (6)	10	0.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9393	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1573	16.7%
e	1052	11.2%
t	808	8.6%
a	590	6.3%
o	562	6.0%
s	507	5.4%
n	467	5.0%
i	450	4.8%
h	414	4.4%
r	378	4.0%
Other values (48)	2592	27.6%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

study_2_ethic_acc
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Somewhat acceptable	133
Completely acceptable	116
Somewhat unacceptable	111
Neutral	82
Completely unacceptable	57

Length

Max length	23
Median length	21
Mean length	18.39478958
Min length	7

Characters and Unicode

Total characters	9179
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Completely acceptable
3rd row	Completely acceptable
4th row	Somewhat acceptable
5th row	Completely acceptable

Common Values

Value	Count	Frequency (%)
Somewhat acceptable	133	26.7%
Completely acceptable	116	23.2%
Somewhat unacceptable	111	22.2%
Neutral	82	16.4%
Completely unacceptable	57	11.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
acceptable	249	27.2%
somewhat	244	26.6%
completely	173	18.9%
unacceptable	168	18.3%
neutral	82	9.0%

Most occurring characters

Value	Count	Frequency (%)
e	1506	16.4%
a	1160	12.6%
t	916	10.0%
l	845	9.2%
c	834	9.1%
p	590	6.4%
b	417	4.5%
m	417	4.5%
	417	4.5%
o	417	4.5%
Other values (9)	1660	18.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8263	90.0%
Uppercase Letter	499	5.4%
Space Separator	417	4.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1506	18.2%
a	1160	14.0%
t	916	11.1%
l	845	10.2%
c	834	10.1%
p	590	7.1%
b	417	5.0%
m	417	5.0%
o	417	5.0%
u	250	3.0%
Other values (5)	911	11.0%

Uppercase Letter

Value	Count	Frequency (%)
S	244	48.9%
C	173	34.7%
N	82	16.4%

Space Separator

Value	Count	Frequency (%)
	417	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8762	95.5%
Common	417	4.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1506	17.2%
a	1160	13.2%
t	916	10.5%
l	845	9.6%
c	834	9.5%
p	590	6.7%
b	417	4.8%
m	417	4.8%
o	417	4.8%
u	250	2.9%
Other values (8)	1410	16.1%

Common

Value	Count	Frequency (%)
	417	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9179	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1506	16.4%
a	1160	12.6%
t	916	10.0%
l	845	9.2%
c	834	9.1%
p	590	6.4%
b	417	4.5%
m	417	4.5%
	417	4.5%
o	417	4.5%
Other values (9)	1660	18.1%

study_2_conc
Categorical

HIGH CARDINALITY
MISSING

Distinct	283
Distinct (%)	91.9%
Missing	191
Missing (%)	38.3%
Memory size	4.0 KiB

na	19
Na	8
Slightly unethical to not alert participants they are part of a research study	1
I think it would have been more ethical if the researchers had simply commented on the public post rather than send an unsolicited private message.	1
They should not have private messaged the users	1
Other values (278)	278

Length

Max length	647
Median length	187
Mean length	113.0357143
Min length	2

Characters and Unicode

Total characters	34815
Distinct characters	66
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	281 ?
Unique (%)	91.2%

Sample

1st row	Going to the poster privately provided opportunity for change without the possibly of increased toxicity from users. I prefer this method over commenting the "correct information".
2nd row	I feel as though, in the above case, users had a choice to respond or not so I think it was honest.
3rd row	na
4th row	It's perfectly within someone's right to send someone else a message on any platform, therefore I believe this study was acceptable.
5th row	This is unethical because those involved were not adequately informed of the researchers intent.

Common Values

Value	Count	Frequency (%)
na	19	3.8%
Na	8	1.6%
Slightly unethical to not alert participants they are part of a research study	1	0.2%
I think it would have been more ethical if the researchers had simply commented on the public post rather than send an unsolicited private message.	1	0.2%
They should not have private messaged the users	1	0.2%
Sending someone a private message does give me concern, that the recipient might have confused the sender for someone they know. However, the results were interesting to say the least.	1	0.2%
My concern with this study is that the participants were not aware of the research study being conducted.	1	0.2%
Participants should be informed they are part of a research project upfront. The use of bots to automate their process I disagree with.	1	0.2%
Using bots on unaware citizens without their consent would seem to be unethical. Not to mention the famous case of the LinkedIn founder funding the troll farm "research" to manipulate the Alabama special election. Unethical if not criminal.	1	0.2%
Not ethical at all to me. I totally find this is not ethical to have users information collected in this way. Especially being a responder on Prolific and seeing all of the verbiage that we have to read and agree to and learning about the things that researchers have to do to maintain a good pool of respondents, this does not make sense at all to me.	1	0.2%
Other values (273)	273	54.7%
(Missing)	191	38.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	285	4.7%
to	170	2.8%
i	143	2.4%
a	142	2.4%
of	135	2.2%
they	132	2.2%
that	126	2.1%
not	122	2.0%
study	102	1.7%
is	102	1.7%
Other values (1023)	4560	75.8%

Most occurring characters

Value	Count	Frequency (%)
	5798	16.7%
e	3664	10.5%
t	2988	8.6%
a	2259	6.5%
i	2007	5.8%
n	1963	5.6%
s	1943	5.6%
o	1924	5.5%
r	1584	4.5%
h	1471	4.2%
Other values (56)	9214	26.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	27758	79.7%
Space Separator	5798	16.7%
Other Punctuation	693	2.0%
Uppercase Letter	506	1.5%
Dash Punctuation	32	0.1%
Close Punctuation	9	< 0.1%
Open Punctuation	8	< 0.1%
Control	4	< 0.1%
Final Punctuation	4	< 0.1%
Decimal Number	3	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3664	13.2%
t	2988	10.8%
a	2259	8.1%
i	2007	7.2%
n	1963	7.1%
s	1943	7.0%
o	1924	6.9%
r	1584	5.7%
h	1471	5.3%
d	992	3.6%
Other values (16)	6963	25.1%

Uppercase Letter

Value	Count	Frequency (%)
I	198	39.1%
T	104	20.6%
A	36	7.1%
P	31	6.1%
N	20	4.0%
M	15	3.0%
S	15	3.0%
W	14	2.8%
O	11	2.2%
D	10	2.0%
Other values (12)	52	10.3%

Other Punctuation

Value	Count	Frequency (%)
.	353	50.9%
,	142	20.5%
'	132	19.0%
"	34	4.9%
?	21	3.0%
/	6	0.9%
;	3	0.4%
!	1	0.1%
:	1	0.1%

Decimal Number

Value	Count	Frequency (%)
4	1	33.3%
2	1	33.3%
1	1	33.3%

Space Separator

Value	Count	Frequency (%)
	5798	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	32	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	9	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	8	100.0%

Control

Value	Count	Frequency (%)
	4	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	28264	81.2%
Common	6551	18.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3664	13.0%
t	2988	10.6%
a	2259	8.0%
i	2007	7.1%
n	1963	6.9%
s	1943	6.9%
o	1924	6.8%
r	1584	5.6%
h	1471	5.2%
d	992	3.5%
Other values (38)	7469	26.4%

Common

Value	Count	Frequency (%)
	5798	88.5%
.	353	5.4%
,	142	2.2%
'	132	2.0%
"	34	0.5%
-	32	0.5%
?	21	0.3%
)	9	0.1%
(	8	0.1%
/	6	0.1%
Other values (8)	16	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	34811	> 99.9%
Punctuation	4	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	5798	16.7%
e	3664	10.5%
t	2988	8.6%
a	2259	6.5%
i	2007	5.8%
n	1963	5.6%
s	1943	5.6%
o	1924	5.5%
r	1584	4.6%
h	1471	4.2%
Other values (55)	9210	26.5%

Punctuation

Value	Count	Frequency (%)
’	4	100.0%

study_2_add_info
Categorical

HIGH CARDINALITY
MISSING

Distinct	109
Distinct (%)	75.7%
Missing	355
Missing (%)	71.1%
Memory size	4.0 KiB

na	29
Na	8
Some people may not want to be contacted privately.	1
The researchers should have also tested liberal/progressive users who posted links to liberal websites that were also allegedly "untrustworthy."	1
The part about not telling people that they are part of a research study because misinformation is spread too much and now the participants will likely believe things that are untrue.	1
Other values (104)	104

Length

Max length	403
Median length	195.5
Mean length	67.75
Min length	2

Characters and Unicode

Total characters	9756
Distinct characters	65
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	107 ?
Unique (%)	74.3%

Sample

1st row	na
2nd row	Concerns over the possibility of the researchers having their own political agenda. Yet fake news is a major problem. What social media really is when mass sharing news (political news), is simple propaganda from the left and right.
3rd row	Full disclosure if research intent
4th row	How do you pick a representative sample on a non representative platform.
5th row	Nz

Common Values

Value	Count	Frequency (%)
na	29	5.8%
Na	8	1.6%
Some people may not want to be contacted privately.	1	0.2%
The researchers should have also tested liberal/progressive users who posted links to liberal websites that were also allegedly "untrustworthy."	1	0.2%
The part about not telling people that they are part of a research study because misinformation is spread too much and now the participants will likely believe things that are untrue.	1	0.2%
Of course there is always the concern about funding and bias. I wonder if because of the private messaging whether there was any supplemental back-and-forth between the unwilling participant and someone behind the study which has potential for some form of abuse or corruption of data.	1	0.2%
I'd like to know the exact messages they are sending.	1	0.2%
you need people consent to do a study on them	1	0.2%
If I found out who labeled the misinformation(same people who labeled Hunter's laptop?) and if I found out that left leaning were also studied.	1	0.2%
N/A/	1	0.2%
Other values (99)	99	19.8%
(Missing)	355	71.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	102	5.9%
i	47	2.7%
to	40	2.3%
na	39	2.3%
of	36	2.1%
a	36	2.1%
they	36	2.1%
if	34	2.0%
would	31	1.8%
that	30	1.7%
Other values (507)	1287	74.9%

Most occurring characters

Value	Count	Frequency (%)
	1600	16.4%
e	999	10.2%
t	809	8.3%
a	645	6.6%
o	543	5.6%
i	535	5.5%
s	529	5.4%
n	512	5.2%
r	432	4.4%
h	403	4.1%
Other values (55)	2749	28.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7769	79.6%
Space Separator	1600	16.4%
Uppercase Letter	181	1.9%
Other Punctuation	169	1.7%
Dash Punctuation	10	0.1%
Open Punctuation	8	0.1%
Close Punctuation	8	0.1%
Decimal Number	7	0.1%
Control	3	< 0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	999	12.9%
t	809	10.4%
a	645	8.3%
o	543	7.0%
i	535	6.9%
s	529	6.8%
n	512	6.6%
r	432	5.6%
h	403	5.2%
l	330	4.2%
Other values (16)	2032	26.2%

Uppercase Letter

Value	Count	Frequency (%)
I	72	39.8%
T	20	11.0%
N	20	11.0%
A	10	5.5%
W	9	5.0%
H	7	3.9%
S	6	3.3%
D	5	2.8%
R	4	2.2%
P	4	2.2%
Other values (10)	24	13.3%

Other Punctuation

Value	Count	Frequency (%)
.	87	51.5%
,	32	18.9%
'	19	11.2%
?	11	6.5%
/	10	5.9%
"	8	4.7%
!	1	0.6%
;	1	0.6%

Decimal Number

Value	Count	Frequency (%)
0	4	57.1%
1	2	28.6%
2	1	14.3%

Open Punctuation

Value	Count	Frequency (%)
(	7	87.5%
[	1	12.5%

Close Punctuation

Value	Count	Frequency (%)
)	7	87.5%
]	1	12.5%

Space Separator

Value	Count	Frequency (%)
	1600	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	10	100.0%

Control

Value	Count	Frequency (%)
	3	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7950	81.5%
Common	1806	18.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	999	12.6%
t	809	10.2%
a	645	8.1%
o	543	6.8%
i	535	6.7%
s	529	6.7%
n	512	6.4%
r	432	5.4%
h	403	5.1%
l	330	4.2%
Other values (36)	2213	27.8%

Common

Value	Count	Frequency (%)
	1600	88.6%
.	87	4.8%
,	32	1.8%
'	19	1.1%
?	11	0.6%
-	10	0.6%
/	10	0.6%
"	8	0.4%
(	7	0.4%
)	7	0.4%
Other values (9)	15	0.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9755	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1600	16.4%
e	999	10.2%
t	809	8.3%
a	645	6.6%
o	543	5.6%
i	535	5.5%
s	529	5.4%
n	512	5.2%
r	432	4.4%
h	403	4.1%
Other values (54)	2748	28.2%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

study_3_ethic_acc
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Completely accepatable	249
Somewhat acceptable	134
Neutral	56
Somewhat unacceptable	39
Completely unacceptable	21

Length

Max length	23
Median length	22
Mean length	19.4749499
Min length	7

Characters and Unicode

Total characters	9718
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Completely accepatable
3rd row	Somewhat acceptable
4th row	Somewhat acceptable
5th row	Completely unacceptable

Common Values

Value	Count	Frequency (%)
Completely accepatable	249	49.9%
Somewhat acceptable	134	26.9%
Neutral	56	11.2%
Somewhat unacceptable	39	7.8%
Completely unacceptable	21	4.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
completely	270	28.7%
accepatable	249	26.4%
somewhat	173	18.4%
acceptable	134	14.2%
unacceptable	60	6.4%
neutral	56	5.9%

Most occurring characters

Value	Count	Frequency (%)
e	1655	17.0%
a	1364	14.0%
l	1039	10.7%
t	942	9.7%
c	886	9.1%
p	713	7.3%
m	443	4.6%
b	443	4.6%
	443	4.6%
o	443	4.6%
Other values (9)	1347	13.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8776	90.3%
Uppercase Letter	499	5.1%
Space Separator	443	4.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1655	18.9%
a	1364	15.5%
l	1039	11.8%
t	942	10.7%
c	886	10.1%
p	713	8.1%
m	443	5.0%
b	443	5.0%
o	443	5.0%
y	270	3.1%
Other values (5)	578	6.6%

Uppercase Letter

Value	Count	Frequency (%)
C	270	54.1%
S	173	34.7%
N	56	11.2%

Space Separator

Value	Count	Frequency (%)
	443	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9275	95.4%
Common	443	4.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1655	17.8%
a	1364	14.7%
l	1039	11.2%
t	942	10.2%
c	886	9.6%
p	713	7.7%
m	443	4.8%
b	443	4.8%
o	443	4.8%
C	270	2.9%
Other values (8)	1077	11.6%

Common

Value	Count	Frequency (%)
	443	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9718	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1655	17.0%
a	1364	14.0%
l	1039	10.7%
t	942	9.7%
c	886	9.1%
p	713	7.3%
m	443	4.6%
b	443	4.6%
	443	4.6%
o	443	4.6%
Other values (9)	1347	13.9%

study_3_conc
Categorical

HIGH CARDINALITY
MISSING

Distinct	247
Distinct (%)	90.1%
Missing	225
Missing (%)	45.1%
Memory size	4.0 KiB

na	21
Na	8
This is more acceptable because participants are informed ahead of time about the use of their data.	1
Bribery to sway someone's oppinion is not very ethical. Worse the research study has no value, because the results of the data was tainted or biased.	1
This type of information gathering requires the Twitter user to agree to their data being used, so it is not unlike any other online study, so this is okay with me as far as ethics. By users agreeing to their data being used, my thought is it probably rules out all of the bots that are so prevalent on Twitter.	1
Other values (242)	242

Length

Max length	732
Median length	154
Mean length	107.7591241
Min length	1

Characters and Unicode

Total characters	29526
Distinct characters	74
Distinct categories	11 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	245 ?
Unique (%)	89.4%

Sample

1st row	I find this is ethical as long as participants were fully aware of what was being monitored. The results are interesting! No concerns.
2nd row	As long as the Facebook users were informed that they would be in a study I feel it is fair. It was up to the users whether they wanted to participate or not. Also, they were encouraged, but not actually made to Like the Facebook study.
3rd row	The web extension being used was invasive, even if it was used with consent. The people participating in the study are not educated enough on exactly how much information the web extension was taking.
4th row	na
5th row	The researchers seem in some ways to try manipulating political viewpoints in a segment of the population for the sake of science.

Common Values

Value	Count	Frequency (%)
na	21	4.2%
Na	8	1.6%
This is more acceptable because participants are informed ahead of time about the use of their data.	1	0.2%
Bribery to sway someone's oppinion is not very ethical. Worse the research study has no value, because the results of the data was tainted or biased.	1	0.2%
This type of information gathering requires the Twitter user to agree to their data being used, so it is not unlike any other online study, so this is okay with me as far as ethics. By users agreeing to their data being used, my thought is it probably rules out all of the bots that are so prevalent on Twitter.	1	0.2%
The study was completely transparent.	1	0.2%
Most importantly, the researchers got the approval of their users first. At least the users were aware that they were taking part in a study even though, in my mind, they were severely underpaid.	1	0.2%
Researchers were up front and honest and people got to pick their reward.	1	0.2%
I feel that since all the participants were informed of the process and offered compensation and they willingly participated, there are not any unethical practices used in the process.	1	0.2%
My only concern is that the browser extension allowed researches to see ALL of the users' posts. I think this would be okay if it was explicitly consented to by the participants, though the above doesn't specify.	1	0.2%
Other values (237)	237	47.5%
(Missing)	225	45.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	336	6.5%
to	164	3.2%
i	130	2.5%
of	120	2.3%
they	106	2.1%
were	105	2.0%
a	98	1.9%
and	98	1.9%
that	96	1.9%
study	93	1.8%
Other values (902)	3791	73.8%

Most occurring characters

Value	Count	Frequency (%)
	4940	16.7%
e	3210	10.9%
t	2556	8.7%
a	1844	6.2%
i	1723	5.8%
o	1663	5.6%
s	1589	5.4%
n	1567	5.3%
r	1354	4.6%
h	1214	4.1%
Other values (64)	7866	26.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	23582	79.9%
Space Separator	4940	16.7%
Other Punctuation	520	1.8%
Uppercase Letter	434	1.5%
Decimal Number	20	0.1%
Dash Punctuation	10	< 0.1%
Currency Symbol	8	< 0.1%
Close Punctuation	5	< 0.1%
Open Punctuation	5	< 0.1%
Control	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3210	13.6%
t	2556	10.8%
a	1844	7.8%
i	1723	7.3%
o	1663	7.1%
s	1589	6.7%
n	1567	6.6%
r	1354	5.7%
h	1214	5.1%
l	841	3.6%
Other values (16)	6021	25.5%

Uppercase Letter

Value	Count	Frequency (%)
I	161	37.1%
T	103	23.7%
A	33	7.6%
N	16	3.7%
P	15	3.5%
S	14	3.2%
E	11	2.5%
F	11	2.5%
B	9	2.1%
L	8	1.8%
Other values (14)	53	12.2%

Other Punctuation

Value	Count	Frequency (%)
.	301	57.9%
,	127	24.4%
'	62	11.9%
"	12	2.3%
?	10	1.9%
/	3	0.6%
!	2	0.4%
;	1	0.2%
:	1	0.2%
%	1	0.2%

Decimal Number

Value	Count	Frequency (%)
0	6	30.0%
5	6	30.0%
8	4	20.0%
3	2	10.0%
2	1	5.0%
1	1	5.0%

Dash Punctuation

Value	Count	Frequency (%)
-	8	80.0%
—	2	20.0%

Space Separator

Value	Count	Frequency (%)
	4940	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	8	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	5	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	5	100.0%

Control

Value	Count	Frequency (%)
	1	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	24016	81.3%
Common	5510	18.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3210	13.4%
t	2556	10.6%
a	1844	7.7%
i	1723	7.2%
o	1663	6.9%
s	1589	6.6%
n	1567	6.5%
r	1354	5.6%
h	1214	5.1%
l	841	3.5%
Other values (40)	6455	26.9%

Common

Value	Count	Frequency (%)
	4940	89.7%
.	301	5.5%
,	127	2.3%
'	62	1.1%
"	12	0.2%
?	10	0.2%
-	8	0.1%
$	8	0.1%
0	6	0.1%
5	6	0.1%
Other values (14)	30	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	29523	> 99.9%
Punctuation	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	4940	16.7%
e	3210	10.9%
t	2556	8.7%
a	1844	6.2%
i	1723	5.8%
o	1663	5.6%
s	1589	5.4%
n	1567	5.3%
r	1354	4.6%
h	1214	4.1%
Other values (62)	7863	26.6%

Punctuation

Value	Count	Frequency (%)
—	2	66.7%
’	1	33.3%

study_3_add_info
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	92
Distinct (%)	71.9%
Missing	371
Missing (%)	74.3%
Memory size	4.0 KiB

na	25
Na	11
no	2
NA.	2
I'd like to know how they can make sure the browser extension only stays on peoples browsers for 8 weeks. Does the user have to remove it themselves? Some people are not so great with technology and wouldn't be able to figure it out and thus the researchers could collect far more data than promised.	1
Other values (87)	87

Length

Max length	300
Median length	186
Mean length	70.265625
Min length	2

Characters and Unicode

Total characters	8994
Distinct characters	61
Distinct categories	11 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	88 ?
Unique (%)	68.8%

Sample

1st row	Making the source code for the web extension publicly available to have complete transparency over what the extension was doing.
2nd row	na
3rd row	Since the study is revealed as a study, I think it’s ethical, but mostly nonsensical
4th row	Na
5th row	Na

Common Values

Value	Count	Frequency (%)
na	25	5.0%
Na	11	2.2%
no	2	0.4%
NA.	2	0.4%
I'd like to know how they can make sure the browser extension only stays on peoples browsers for 8 weeks. Does the user have to remove it themselves? Some people are not so great with technology and wouldn't be able to figure it out and thus the researchers could collect far more data than promised.	1	0.2%
I would not click on the ad	1	0.2%
I think the information is going to be skewed based upon what the user thinks the researcher is looking for. They are also more likely to click on political sites because they want to make sure that the researcher is gathering enough data from them.	1	0.2%
What was collected by the extension would be valuable.	1	0.2%
So many ways for people to get manipulated with these studies I agree with this one being one of the good ones.	1	0.2%
Everyone was informed, so i think this is a good study.	1	0.2%
Other values (82)	82	16.4%
(Missing)	371	74.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	125	7.7%
to	48	3.0%
i	42	2.6%
na	39	2.4%
would	38	2.3%
of	30	1.8%
that	28	1.7%
was	26	1.6%
it	24	1.5%
study	24	1.5%
Other values (469)	1198	73.9%

Most occurring characters

Value	Count	Frequency (%)
	1509	16.8%
e	962	10.7%
t	770	8.6%
a	600	6.7%
o	562	6.2%
s	458	5.1%
n	450	5.0%
i	441	4.9%
r	392	4.4%
h	358	4.0%
Other values (51)	2492	27.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7170	79.7%
Space Separator	1509	16.8%
Other Punctuation	146	1.6%
Uppercase Letter	138	1.5%
Decimal Number	10	0.1%
Close Punctuation	5	0.1%
Open Punctuation	5	0.1%
Dash Punctuation	4	< 0.1%
Control	3	< 0.1%
Currency Symbol	3	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	962	13.4%
t	770	10.7%
a	600	8.4%
o	562	7.8%
s	458	6.4%
n	450	6.3%
i	441	6.2%
r	392	5.5%
h	358	5.0%
l	291	4.1%
Other values (15)	1886	26.3%

Uppercase Letter

Value	Count	Frequency (%)
I	57	41.3%
N	19	13.8%
T	13	9.4%
A	11	8.0%
W	7	5.1%
S	5	3.6%
D	4	2.9%
M	4	2.9%
H	3	2.2%
E	3	2.2%
Other values (8)	12	8.7%

Other Punctuation

Value	Count	Frequency (%)
.	83	56.8%
,	34	23.3%
'	21	14.4%
?	5	3.4%
!	1	0.7%
&	1	0.7%
/	1	0.7%

Decimal Number

Value	Count	Frequency (%)
5	5	50.0%
8	3	30.0%
1	1	10.0%
0	1	10.0%

Space Separator

Value	Count	Frequency (%)
	1509	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	5	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	5	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	4	100.0%

Control

Value	Count	Frequency (%)
	3	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	3	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7308	81.3%
Common	1686	18.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	962	13.2%
t	770	10.5%
a	600	8.2%
o	562	7.7%
s	458	6.3%
n	450	6.2%
i	441	6.0%
r	392	5.4%
h	358	4.9%
l	291	4.0%
Other values (33)	2024	27.7%

Common

Value	Count	Frequency (%)
	1509	89.5%
.	83	4.9%
,	34	2.0%
'	21	1.2%
5	5	0.3%
)	5	0.3%
(	5	0.3%
?	5	0.3%
-	4	0.2%
	3	0.2%
Other values (8)	12	0.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8993	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1509	16.8%
e	962	10.7%
t	770	8.6%
a	600	6.7%
o	562	6.2%
s	458	5.1%
n	450	5.0%
i	441	4.9%
r	392	4.4%
h	358	4.0%
Other values (50)	2491	27.7%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

study_4_ethic_acc
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Somewhat acceptable	121
Completely acceptable	115
Somewhat unacceptable	110
Neutral	88
Completely unacceptable	65

Length

Max length	23
Median length	21
Mean length	18.30661323
Min length	7

Characters and Unicode

Total characters	9135
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Neutral
3rd row	Somewhat unacceptable
4th row	Somewhat unacceptable
5th row	Completely acceptable

Common Values

Value	Count	Frequency (%)
Somewhat acceptable	121	24.2%
Completely acceptable	115	23.0%
Somewhat unacceptable	110	22.0%
Neutral	88	17.6%
Completely unacceptable	65	13.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
acceptable	236	25.9%
somewhat	231	25.4%
completely	180	19.8%
unacceptable	175	19.2%
neutral	88	9.7%

Most occurring characters

Value	Count	Frequency (%)
e	1501	16.4%
a	1141	12.5%
t	910	10.0%
l	859	9.4%
c	822	9.0%
p	591	6.5%
b	411	4.5%
m	411	4.5%
	411	4.5%
o	411	4.5%
Other values (9)	1667	18.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8225	90.0%
Uppercase Letter	499	5.5%
Space Separator	411	4.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1501	18.2%
a	1141	13.9%
t	910	11.1%
l	859	10.4%
c	822	10.0%
p	591	7.2%
b	411	5.0%
m	411	5.0%
o	411	5.0%
u	263	3.2%
Other values (5)	905	11.0%

Uppercase Letter

Value	Count	Frequency (%)
S	231	46.3%
C	180	36.1%
N	88	17.6%

Space Separator

Value	Count	Frequency (%)
	411	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8724	95.5%
Common	411	4.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1501	17.2%
a	1141	13.1%
t	910	10.4%
l	859	9.8%
c	822	9.4%
p	591	6.8%
b	411	4.7%
m	411	4.7%
o	411	4.7%
u	263	3.0%
Other values (8)	1404	16.1%

Common

Value	Count	Frequency (%)
	411	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9135	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1501	16.4%
a	1141	12.5%
t	910	10.0%
l	859	9.4%
c	822	9.0%
p	591	6.5%
b	411	4.5%
m	411	4.5%
	411	4.5%
o	411	4.5%
Other values (9)	1667	18.2%

study_4_conc
Categorical

HIGH CARDINALITY
MISSING

Distinct	267
Distinct (%)	90.2%
Missing	203
Missing (%)	40.7%
Memory size	4.0 KiB

na	24
Na	7
Since this was done in a public setting, I feel like it is more ethically acceptable than the other study that was similar, but done through private messages.	1
I think any time you are purposely deceiving people and not informing them about what the true reasoning/outliner is, there is an ethical dilemma.	1
Wow, just goes to show that social media feeds on itself. A public message is determined immediately to be hostile - both to the original poster and their followers who maintain similar opinions. I have seen this play out on facebook where the language gets so violent and inappropriate, I have snoozed people. This happened rather frequently with covid responses and supporters of T's big lie.	1
Other values (262)	262

Length

Max length	822
Median length	222
Mean length	122.1756757
Min length	2

Characters and Unicode

Total characters	36164
Distinct characters	77
Distinct categories	13 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	265 ?
Unique (%)	89.5%

Sample

1st row	I am uncertain how I feel completely about a researcher creating a fake account. However I do understand the desire to protect themselves and to not give away their actions as being part of a study. This misinformation needed to be corrected for the public but it opened the original poster to toxicity. The OP may not have known it was incorrect.
2nd row	Users were not aware of what was going on so they were possibly more honest in their opinions because they had no idea they were being analyzed.
3rd row	na
4th row	Many of the people that have large political followings on twitter (and many who don't) often know already the news they are sharing is fake. It's political partisanship and the spreading of propaganda. Some might post fake news only to gain more followers (the masses) if they believe it serves that end.
5th row	This study is unethfull disclosure of intent of research.ical because they were not informed of the research study

Common Values

Value	Count	Frequency (%)
na	24	4.8%
Na	7	1.4%
Since this was done in a public setting, I feel like it is more ethically acceptable than the other study that was similar, but done through private messages.	1	0.2%
I think any time you are purposely deceiving people and not informing them about what the true reasoning/outliner is, there is an ethical dilemma.	1	0.2%
Wow, just goes to show that social media feeds on itself. A public message is determined immediately to be hostile - both to the original poster and their followers who maintain similar opinions. I have seen this play out on facebook where the language gets so violent and inappropriate, I have snoozed people. This happened rather frequently with covid responses and supporters of T's big lie.	1	0.2%
I have found that some "fact-checking" sites end up having wrong information as well. A great case is how the idea that masks don't work spread. Many studies have been conducted and some very reputable scientific organizations have studies on their websites that say masks do not work, but the data is typically very small or the type of mask used was a very thin cloth mask, but it gives fuel to people who spread the false information that masking does not work.	1	0.2%
I feel like this is acceptable because when you sign up for social media, if you are posting something publicly it is assumed that anyone can look at these posts and reply to them	1	0.2%
This is all public, so I have no issue with the researchers observing this.	1	0.2%
I personally have no issue with this study, though objectively it seems a little dubious to study individuals without their knowledge like this.	1	0.2%
I object to most studies in which users are not informed that they are being studied and that they are being manipulated. Secondly, I would like to see the type of reply that was originally sent. This study almost directly contradicts the results reached in the last study in which people deleted their hate speech because they got a link to a fact checking site along with an empathetic response. Lastly, I now do my own fact checking after learning that quite a few of these fact checkers are deliberately manipulating and distorting info because of their own bias. I simply no longer trust the "fact checkers."	1	0.2%
Other values (257)	257	51.5%
(Missing)	203	40.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	302	4.8%
i	156	2.5%
to	152	2.4%
that	148	2.4%
of	148	2.4%
they	137	2.2%
a	137	2.2%
is	121	1.9%
and	112	1.8%
not	111	1.8%
Other values (1094)	4764	75.8%

Most occurring characters

Value	Count	Frequency (%)
	6069	16.8%
e	3621	10.0%
t	3134	8.7%
a	2366	6.5%
i	2149	5.9%
n	2056	5.7%
o	2034	5.6%
s	1859	5.1%
r	1541	4.3%
h	1495	4.1%
Other values (67)	9840	27.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	28746	79.5%
Space Separator	6069	16.8%
Other Punctuation	721	2.0%
Uppercase Letter	516	1.4%
Dash Punctuation	34	0.1%
Decimal Number	34	0.1%
Open Punctuation	10	< 0.1%
Control	9	< 0.1%
Close Punctuation	9	< 0.1%
Final Punctuation	6	< 0.1%
Other values (3)	10	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3621	12.6%
t	3134	10.9%
a	2366	8.2%
i	2149	7.5%
n	2056	7.2%
o	2034	7.1%
s	1859	6.5%
r	1541	5.4%
h	1495	5.2%
d	1009	3.5%
Other values (16)	7482	26.0%

Uppercase Letter

Value	Count	Frequency (%)
I	202	39.1%
T	112	21.7%
A	32	6.2%
P	23	4.5%
S	22	4.3%
N	18	3.5%
M	14	2.7%
W	13	2.5%
O	11	2.1%
D	8	1.6%
Other values (12)	61	11.8%

Other Punctuation

Value	Count	Frequency (%)
.	352	48.8%
,	176	24.4%
'	107	14.8%
"	46	6.4%
?	13	1.8%
/	11	1.5%
%	9	1.2%
&	3	0.4%
:	2	0.3%
;	1	0.1%

Decimal Number

Value	Count	Frequency (%)
2	13	38.2%
0	12	35.3%
7	2	5.9%
3	2	5.9%
5	2	5.9%
4	1	2.9%
9	1	2.9%
1	1	2.9%

Math Symbol

Value	Count	Frequency (%)
=	4	80.0%
~	1	20.0%

Space Separator

Value	Count	Frequency (%)
	6069	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	34	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	10	100.0%

Control

Value	Count	Frequency (%)
	9	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	9	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	6	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	4	100.0%

Initial Punctuation

Value	Count	Frequency (%)
‘	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	29262	80.9%
Common	6902	19.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3621	12.4%
t	3134	10.7%
a	2366	8.1%
i	2149	7.3%
n	2056	7.0%
o	2034	7.0%
s	1859	6.4%
r	1541	5.3%
h	1495	5.1%
d	1009	3.4%
Other values (38)	7998	27.3%

Common

Value	Count	Frequency (%)
	6069	87.9%
.	352	5.1%
,	176	2.5%
'	107	1.6%
"	46	0.7%
-	34	0.5%
?	13	0.2%
2	13	0.2%
0	12	0.2%
/	11	0.2%
Other values (19)	69	1.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	36157	> 99.9%
Punctuation	7	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	6069	16.8%
e	3621	10.0%
t	3134	8.7%
a	2366	6.5%
i	2149	5.9%
n	2056	5.7%
o	2034	5.6%
s	1859	5.1%
r	1541	4.3%
h	1495	4.1%
Other values (65)	9833	27.2%

Punctuation

Value	Count	Frequency (%)
’	6	85.7%
‘	1	14.3%

study_4_add_info
Categorical

HIGH CARDINALITY
MISSING

Distinct	104
Distinct (%)	72.2%
Missing	355
Missing (%)	71.1%
Memory size	4.0 KiB

na	32
Na	8
no	3
I don't think it's acceptable because non human bots were used and even though they linked to a fact checking website, they are still influencing people and that will cause more divisiveness.	1
The researchers relied on "fact checkers" to determine if the information was "fake" or not. If there existed a bias or margin of error in the fact checker's process then the researchers would be working with wrong information themselves.	1
Other values (99)	99

Length

Max length	580
Median length	189
Mean length	71.40972222
Min length	2

Characters and Unicode

Total characters	10283
Distinct characters	60
Distinct categories	9 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	101 ?
Unique (%)	70.1%

Sample

1st row	The researchers had a purpose in seeing the responses of those interacting with the post. I do not agree with how it was done entirely however I do not know a better way to get the results that were desired.
2nd row	na
3rd row	Full disclosure of intent of researchers.
4th row	Please see comments from the first study
5th row	Na

Common Values

Value	Count	Frequency (%)
na	32	6.4%
Na	8	1.6%
no	3	0.6%
I don't think it's acceptable because non human bots were used and even though they linked to a fact checking website, they are still influencing people and that will cause more divisiveness.	1	0.2%
The researchers relied on "fact checkers" to determine if the information was "fake" or not. If there existed a bias or margin of error in the fact checker's process then the researchers would be working with wrong information themselves.	1	0.2%
I find it acceptable because this method is helping to slow the spread of misinformation	1	0.2%
The outcome of this study has also resulted in negative behavior by the participants due to the experiment.	1	0.2%
Completely unethical. Customers were unaware of the study and fake accounts were made. No one was compensated or aware.	1	0.2%
I would like to know example content of the tweets and be shown an example of the bot accounts - it's hard to know exactly how individuals would react to someone pointing out their wrong without knowing the profile of the person pointing out the error.	1	0.2%
None.	1	0.2%
Other values (94)	94	18.8%
(Missing)	355	71.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	98	5.3%
to	60	3.2%
i	44	2.4%
na	43	2.3%
of	37	2.0%
it	36	1.9%
a	33	1.8%
if	32	1.7%
they	31	1.7%
that	29	1.6%
Other values (534)	1422	76.2%

Most occurring characters

Value	Count	Frequency (%)
	1743	17.0%
e	1097	10.7%
t	899	8.7%
a	650	6.3%
o	585	5.7%
n	525	5.1%
i	524	5.1%
s	524	5.1%
h	449	4.4%
r	438	4.3%
Other values (50)	2849	27.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8164	79.4%
Space Separator	1743	17.0%
Other Punctuation	181	1.8%
Uppercase Letter	171	1.7%
Dash Punctuation	10	0.1%
Close Punctuation	4	< 0.1%
Open Punctuation	4	< 0.1%
Decimal Number	4	< 0.1%
Control	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1097	13.4%
t	899	11.0%
a	650	8.0%
o	585	7.2%
n	525	6.4%
i	524	6.4%
s	524	6.4%
h	449	5.5%
r	438	5.4%
l	311	3.8%
Other values (16)	2162	26.5%

Uppercase Letter

Value	Count	Frequency (%)
I	64	37.4%
T	25	14.6%
N	20	11.7%
W	11	6.4%
R	6	3.5%
A	6	3.5%
P	6	3.5%
B	5	2.9%
M	4	2.3%
F	4	2.3%
Other values (10)	20	11.7%

Other Punctuation

Value	Count	Frequency (%)
.	99	54.7%
,	29	16.0%
'	26	14.4%
"	14	7.7%
?	7	3.9%
/	4	2.2%
;	2	1.1%

Decimal Number

Value	Count	Frequency (%)
0	3	75.0%
2	1	25.0%

Space Separator

Value	Count	Frequency (%)
	1743	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	10	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	4	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	4	100.0%

Control

Value	Count	Frequency (%)
	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8335	81.1%
Common	1948	18.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1097	13.2%
t	899	10.8%
a	650	7.8%
o	585	7.0%
n	525	6.3%
i	524	6.3%
s	524	6.3%
h	449	5.4%
r	438	5.3%
l	311	3.7%
Other values (36)	2333	28.0%

Common

Value	Count	Frequency (%)
	1743	89.5%
.	99	5.1%
,	29	1.5%
'	26	1.3%
"	14	0.7%
-	10	0.5%
?	7	0.4%
)	4	0.2%
(	4	0.2%
/	4	0.2%
Other values (4)	8	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	10283	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1743	17.0%
e	1097	10.7%
t	899	8.7%
a	650	6.3%
o	585	5.7%
n	525	5.1%
i	524	5.1%
s	524	5.1%
h	449	4.4%
r	438	4.3%
Other values (50)	2849	27.7%

design_cont
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Very important	165
Moderately important	124
Extremely important	105
Slightly important	67
Not at all important	38

Length

Max length	20
Median length	19
Mean length	17.53707415
Min length	14

Characters and Unicode

Total characters	8751
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Extremely important
4th row	Moderately important
5th row	Extremely important

Common Values

Value	Count	Frequency (%)
Very important	165	33.1%
Moderately important	124	24.8%
Extremely important	105	21.0%
Slightly important	67	13.4%
Not at all important	38	7.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	46.5%
very	165	15.4%
moderately	124	11.5%
extremely	105	9.8%
slightly	67	6.2%
not	38	3.5%
at	38	3.5%
all	38	3.5%

Most occurring characters

Value	Count	Frequency (%)
t	1370	15.7%
r	893	10.2%
a	699	8.0%
o	661	7.6%
e	623	7.1%
m	604	6.9%
	575	6.6%
i	566	6.5%
p	499	5.7%
n	499	5.7%
Other values (11)	1762	20.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7677	87.7%
Space Separator	575	6.6%
Uppercase Letter	499	5.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1370	17.8%
r	893	11.6%
a	699	9.1%
o	661	8.6%
e	623	8.1%
m	604	7.9%
i	566	7.4%
p	499	6.5%
n	499	6.5%
y	461	6.0%
Other values (5)	802	10.4%

Uppercase Letter

Value	Count	Frequency (%)
V	165	33.1%
M	124	24.8%
E	105	21.0%
S	67	13.4%
N	38	7.6%

Space Separator

Value	Count	Frequency (%)
	575	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8176	93.4%
Common	575	6.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1370	16.8%
r	893	10.9%
a	699	8.5%
o	661	8.1%
e	623	7.6%
m	604	7.4%
i	566	6.9%
p	499	6.1%
n	499	6.1%
y	461	5.6%
Other values (10)	1301	15.9%

Common

Value	Count	Frequency (%)
	575	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8751	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1370	15.7%
r	893	10.2%
a	699	8.0%
o	661	7.6%
e	623	7.1%
m	604	6.9%
	575	6.6%
i	566	6.5%
p	499	5.7%
n	499	5.7%
Other values (11)	1762	20.1%

design_num_users
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Not at all important	125
Very important	106
Slightly important	95
Moderately important	89
Extremely important	84

Length

Max length	20
Median length	19
Mean length	18.17635271
Min length	14

Characters and Unicode

Total characters	9070
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Very important
4th row	Moderately important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Not at all important	125	25.1%
Very important	106	21.2%
Slightly important	95	19.0%
Moderately important	89	17.8%
Extremely important	84	16.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	40.0%
not	125	10.0%
at	125	10.0%
all	125	10.0%
very	106	8.5%
slightly	95	7.6%
moderately	89	7.1%
extremely	84	6.7%

Most occurring characters

Value	Count	Frequency (%)
t	1516	16.7%
a	838	9.2%
r	778	8.6%
	749	8.3%
o	713	7.9%
l	613	6.8%
i	594	6.5%
m	583	6.4%
n	499	5.5%
p	499	5.5%
Other values (11)	1688	18.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7822	86.2%
Space Separator	749	8.3%
Uppercase Letter	499	5.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1516	19.4%
a	838	10.7%
r	778	9.9%
o	713	9.1%
l	613	7.8%
i	594	7.6%
m	583	7.5%
n	499	6.4%
p	499	6.4%
e	452	5.8%
Other values (5)	737	9.4%

Uppercase Letter

Value	Count	Frequency (%)
N	125	25.1%
V	106	21.2%
S	95	19.0%
M	89	17.8%
E	84	16.8%

Space Separator

Value	Count	Frequency (%)
	749	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8321	91.7%
Common	749	8.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1516	18.2%
a	838	10.1%
r	778	9.3%
o	713	8.6%
l	613	7.4%
i	594	7.1%
m	583	7.0%
n	499	6.0%
p	499	6.0%
e	452	5.4%
Other values (10)	1236	14.9%

Common

Value	Count	Frequency (%)
	749	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9070	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1516	16.7%
a	838	9.2%
r	778	8.6%
	749	8.3%
o	713	7.9%
l	613	6.8%
i	594	6.5%
m	583	6.4%
n	499	5.5%
p	499	5.5%
Other values (11)	1688	18.6%

design_res_purp
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Very important	136
Extremely important	121
Moderately important	109
Slightly important	68
Not at all important	65

Length

Max length	20
Median length	19
Mean length	17.8496994
Min length	14

Characters and Unicode

Total characters	8907
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Very important
4th row	Extremely important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Very important	136	27.3%
Extremely important	121	24.2%
Moderately important	109	21.8%
Slightly important	68	13.6%
Not at all important	65	13.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	44.2%
very	136	12.1%
extremely	121	10.7%
moderately	109	9.7%
slightly	68	6.0%
not	65	5.8%
at	65	5.8%
all	65	5.8%

Most occurring characters

Value	Count	Frequency (%)
t	1426	16.0%
r	865	9.7%
a	738	8.3%
o	673	7.6%
	629	7.1%
m	620	7.0%
e	596	6.7%
i	567	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1795	20.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7779	87.3%
Space Separator	629	7.1%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1426	18.3%
r	865	11.1%
a	738	9.5%
o	673	8.7%
m	620	8.0%
e	596	7.7%
i	567	7.3%
p	499	6.4%
n	499	6.4%
l	496	6.4%
Other values (5)	800	10.3%

Uppercase Letter

Value	Count	Frequency (%)
V	136	27.3%
E	121	24.2%
M	109	21.8%
S	68	13.6%
N	65	13.0%

Space Separator

Value	Count	Frequency (%)
	629	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8278	92.9%
Common	629	7.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1426	17.2%
r	865	10.4%
a	738	8.9%
o	673	8.1%
m	620	7.5%
e	596	7.2%
i	567	6.8%
p	499	6.0%
n	499	6.0%
l	496	6.0%
Other values (10)	1299	15.7%

Common

Value	Count	Frequency (%)
	629	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8907	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1426	16.0%
r	865	9.7%
a	738	8.3%
o	673	7.6%
	629	7.1%
m	620	7.0%
e	596	6.7%
i	567	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1795	20.2%

design_len_data
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Very important	120
Moderately important	119
Slightly important	107
Not at all important	97
Extremely important	56

Length

Max length	20
Median length	19
Mean length	18.01603206
Min length	14

Characters and Unicode

Total characters	8990
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Extremely important
4th row	Very important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Very important	120	24.0%
Moderately important	119	23.8%
Slightly important	107	21.4%
Not at all important	97	19.4%
Extremely important	56	11.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	41.9%
very	120	10.1%
moderately	119	10.0%
slightly	107	9.0%
not	97	8.1%
at	97	8.1%
all	97	8.1%
extremely	56	4.7%

Most occurring characters

Value	Count	Frequency (%)
t	1474	16.4%
a	812	9.0%
r	794	8.8%
o	715	8.0%
	693	7.7%
i	606	6.7%
l	583	6.5%
m	555	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1760	19.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7798	86.7%
Space Separator	693	7.7%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1474	18.9%
a	812	10.4%
r	794	10.2%
o	715	9.2%
i	606	7.8%
l	583	7.5%
m	555	7.1%
p	499	6.4%
n	499	6.4%
e	470	6.0%
Other values (5)	791	10.1%

Uppercase Letter

Value	Count	Frequency (%)
V	120	24.0%
M	119	23.8%
S	107	21.4%
N	97	19.4%
E	56	11.2%

Space Separator

Value	Count	Frequency (%)
	693	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8297	92.3%
Common	693	7.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1474	17.8%
a	812	9.8%
r	794	9.6%
o	715	8.6%
i	606	7.3%
l	583	7.0%
m	555	6.7%
p	499	6.0%
n	499	6.0%
e	470	5.7%
Other values (10)	1290	15.5%

Common

Value	Count	Frequency (%)
	693	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8990	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1474	16.4%
a	812	9.0%
r	794	8.8%
o	715	8.0%
	693	7.7%
i	606	6.7%
l	583	6.5%
m	555	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1760	19.6%

design_admin_inter
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Moderately important	139
Very important	109
Slightly important	96
Not at all important	91
Extremely important	64

Length

Max length	20
Median length	19
Mean length	18.17635271
Min length	14

Characters and Unicode

Total characters	9070
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Moderately important
4th row	Moderately important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Moderately important	139	27.9%
Very important	109	21.8%
Slightly important	96	19.2%
Not at all important	91	18.2%
Extremely important	64	12.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	42.3%
moderately	139	11.8%
very	109	9.2%
slightly	96	8.1%
not	91	7.7%
at	91	7.7%
all	91	7.7%
extremely	64	5.4%

Most occurring characters

Value	Count	Frequency (%)
t	1479	16.3%
a	820	9.0%
r	811	8.9%
o	729	8.0%
	681	7.5%
i	595	6.6%
l	577	6.4%
m	563	6.2%
e	515	5.7%
p	499	5.5%
Other values (11)	1801	19.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7890	87.0%
Space Separator	681	7.5%
Uppercase Letter	499	5.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1479	18.7%
a	820	10.4%
r	811	10.3%
o	729	9.2%
i	595	7.5%
l	577	7.3%
m	563	7.1%
e	515	6.5%
p	499	6.3%
n	499	6.3%
Other values (5)	803	10.2%

Uppercase Letter

Value	Count	Frequency (%)
M	139	27.9%
V	109	21.8%
S	96	19.2%
N	91	18.2%
E	64	12.8%

Space Separator

Value	Count	Frequency (%)
	681	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8389	92.5%
Common	681	7.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1479	17.6%
a	820	9.8%
r	811	9.7%
o	729	8.7%
i	595	7.1%
l	577	6.9%
m	563	6.7%
e	515	6.1%
p	499	5.9%
n	499	5.9%
Other values (10)	1302	15.5%

Common

Value	Count	Frequency (%)
	681	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9070	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1479	16.3%
a	820	9.0%
r	811	8.9%
o	729	8.0%
	681	7.5%
i	595	6.6%
l	577	6.4%
m	563	6.2%
e	515	5.7%
p	499	5.5%
Other values (11)	1801	19.9%

design_inter_type
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Very important	162
Moderately important	145
Extremely important	94
Slightly important	65
Not at all important	33

Length

Max length	20
Median length	19
Mean length	17.60320641
Min length	14

Characters and Unicode

Total characters	8784
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Slightly important
2nd row	Not at all important
3rd row	Very important
4th row	Very important
5th row	Extremely important

Common Values

Value	Count	Frequency (%)
Very important	162	32.5%
Moderately important	145	29.1%
Extremely important	94	18.8%
Slightly important	65	13.0%
Not at all important	33	6.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	46.9%
very	162	15.2%
moderately	145	13.6%
extremely	94	8.8%
slightly	65	6.1%
not	33	3.1%
at	33	3.1%
all	33	3.1%

Most occurring characters

Value	Count	Frequency (%)
t	1368	15.6%
r	900	10.2%
a	710	8.1%
o	677	7.7%
e	640	7.3%
m	593	6.8%
	565	6.4%
i	564	6.4%
p	499	5.7%
n	499	5.7%
Other values (11)	1769	20.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7720	87.9%
Space Separator	565	6.4%
Uppercase Letter	499	5.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1368	17.7%
r	900	11.7%
a	710	9.2%
o	677	8.8%
e	640	8.3%
m	593	7.7%
i	564	7.3%
p	499	6.5%
n	499	6.5%
y	466	6.0%
Other values (5)	804	10.4%

Uppercase Letter

Value	Count	Frequency (%)
V	162	32.5%
M	145	29.1%
E	94	18.8%
S	65	13.0%
N	33	6.6%

Space Separator

Value	Count	Frequency (%)
	565	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8219	93.6%
Common	565	6.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1368	16.6%
r	900	11.0%
a	710	8.6%
o	677	8.2%
e	640	7.8%
m	593	7.2%
i	564	6.9%
p	499	6.1%
n	499	6.1%
y	466	5.7%
Other values (10)	1303	15.9%

Common

Value	Count	Frequency (%)
	565	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8784	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1368	15.6%
r	900	10.2%
a	710	8.1%
o	677	7.7%
e	640	7.3%
m	593	6.8%
	565	6.4%
i	564	6.4%
p	499	5.7%
n	499	5.7%
Other values (11)	1769	20.1%

design_partic_aware
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Extremely important	191
Very important	132
Moderately important	92
Slightly important	53
Not at all important	31

Length

Max length	20
Median length	19
Mean length	17.81763527
Min length	14

Characters and Unicode

Total characters	8891
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Slightly important
2nd row	Moderately important
3rd row	Moderately important
4th row	Extremely important
5th row	Slightly important

Common Values

Value	Count	Frequency (%)
Extremely important	191	38.3%
Very important	132	26.5%
Moderately important	92	18.4%
Slightly important	53	10.6%
Not at all important	31	6.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	47.1%
extremely	191	18.0%
very	132	12.5%
moderately	92	8.7%
slightly	53	5.0%
not	31	2.9%
at	31	2.9%
all	31	2.9%

Most occurring characters

Value	Count	Frequency (%)
t	1396	15.7%
r	914	10.3%
e	698	7.9%
m	690	7.8%
a	653	7.3%
o	622	7.0%
	561	6.3%
i	552	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1807	20.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7831	88.1%
Space Separator	561	6.3%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1396	17.8%
r	914	11.7%
e	698	8.9%
m	690	8.8%
a	653	8.3%
o	622	7.9%
i	552	7.0%
p	499	6.4%
n	499	6.4%
y	468	6.0%
Other values (5)	840	10.7%

Uppercase Letter

Value	Count	Frequency (%)
E	191	38.3%
V	132	26.5%
M	92	18.4%
S	53	10.6%
N	31	6.2%

Space Separator

Value	Count	Frequency (%)
	561	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8330	93.7%
Common	561	6.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1396	16.8%
r	914	11.0%
e	698	8.4%
m	690	8.3%
a	653	7.8%
o	622	7.5%
i	552	6.6%
p	499	6.0%
n	499	6.0%
y	468	5.6%
Other values (10)	1339	16.1%

Common

Value	Count	Frequency (%)
	561	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8891	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1396	15.7%
r	914	10.3%
e	698	7.9%
m	690	7.8%
a	653	7.3%
o	622	7.0%
	561	6.3%
i	552	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1807	20.3%

design_inter_impact
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Very important	139
Moderately important	114
Extremely important	107
Slightly important	80
Not at all important	59

Length

Max length	20
Median length	19
Mean length	17.79358717
Min length	14

Characters and Unicode

Total characters	8879
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Extremely important
4th row	Very important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Very important	139	27.9%
Moderately important	114	22.8%
Extremely important	107	21.4%
Slightly important	80	16.0%
Not at all important	59	11.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	44.7%
very	139	12.5%
moderately	114	10.2%
extremely	107	9.6%
slightly	80	7.2%
not	59	5.3%
at	59	5.3%
all	59	5.3%

Most occurring characters

Value	Count	Frequency (%)
t	1417	16.0%
r	859	9.7%
a	731	8.2%
o	672	7.6%
	617	6.9%
m	606	6.8%
e	581	6.5%
i	579	6.5%
l	499	5.6%
p	499	5.6%
Other values (11)	1819	20.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7763	87.4%
Space Separator	617	6.9%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1417	18.3%
r	859	11.1%
a	731	9.4%
o	672	8.7%
m	606	7.8%
e	581	7.5%
i	579	7.5%
l	499	6.4%
p	499	6.4%
n	499	6.4%
Other values (5)	821	10.6%

Uppercase Letter

Value	Count	Frequency (%)
V	139	27.9%
M	114	22.8%
E	107	21.4%
S	80	16.0%
N	59	11.8%

Space Separator

Value	Count	Frequency (%)
	617	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8262	93.1%
Common	617	6.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1417	17.2%
r	859	10.4%
a	731	8.8%
o	672	8.1%
m	606	7.3%
e	581	7.0%
i	579	7.0%
l	499	6.0%
p	499	6.0%
n	499	6.0%
Other values (10)	1320	16.0%

Common

Value	Count	Frequency (%)
	617	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8879	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1417	16.0%
r	859	9.7%
a	731	8.2%
o	672	7.6%
	617	6.9%
m	606	6.8%
e	581	6.5%
i	579	6.5%
l	499	5.6%
p	499	5.6%
Other values (11)	1819	20.5%

design_type_data
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Very important	144
Moderately important	130
Extremely important	106
Slightly important	70
Not at all important	49

Length

Max length	20
Median length	19
Mean length	17.7755511
Min length	14

Characters and Unicode

Total characters	8870
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Not at all important
4th row	Very important
5th row	Extremely important

Common Values

Value	Count	Frequency (%)
Very important	144	28.9%
Moderately important	130	26.1%
Extremely important	106	21.2%
Slightly important	70	14.0%
Not at all important	49	9.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	45.5%
very	144	13.1%
moderately	130	11.9%
extremely	106	9.7%
slightly	70	6.4%
not	49	4.5%
at	49	4.5%
all	49	4.5%

Most occurring characters

Value	Count	Frequency (%)
t	1402	15.8%
r	879	9.9%
a	727	8.2%
o	678	7.6%
e	616	6.9%
m	605	6.8%
	597	6.7%
i	569	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1799	20.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7774	87.6%
Space Separator	597	6.7%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1402	18.0%
r	879	11.3%
a	727	9.4%
o	678	8.7%
e	616	7.9%
m	605	7.8%
i	569	7.3%
p	499	6.4%
n	499	6.4%
l	474	6.1%
Other values (5)	826	10.6%

Uppercase Letter

Value	Count	Frequency (%)
V	144	28.9%
M	130	26.1%
E	106	21.2%
S	70	14.0%
N	49	9.8%

Space Separator

Value	Count	Frequency (%)
	597	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8273	93.3%
Common	597	6.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1402	16.9%
r	879	10.6%
a	727	8.8%
o	678	8.2%
e	616	7.4%
m	605	7.3%
i	569	6.9%
p	499	6.0%
n	499	6.0%
l	474	5.7%
Other values (10)	1325	16.0%

Common

Value	Count	Frequency (%)
	597	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8870	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1402	15.8%
r	879	9.9%
a	727	8.2%
o	678	7.6%
e	616	6.9%
m	605	6.8%
	597	6.7%
i	569	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1799	20.3%

design_add_fac
Categorical

HIGH CARDINALITY
MISSING

Distinct	261
Distinct (%)	65.9%
Missing	103
Missing (%)	20.6%
Memory size	4.0 KiB

No	48
no	41
None	13
na	12
No.	9
Other values (256)	273

Length

Max length	661
Median length	317
Mean length	68.42171717
Min length	2

Characters and Unicode

Total characters	27095
Distinct characters	70
Distinct categories	11 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	250 ?
Unique (%)	63.1%

Sample

1st row	No.
2nd row	The only aspects of social media research that would cause concern for me is saving photographs or imaging data.
3rd row	None that I can think of, other than what has been asked already.
4th row	Reducing any type of hate is always a good thing.
5th row	no

Common Values

Value	Count	Frequency (%)
No	48	9.6%
no	41	8.2%
None	13	2.6%
na	12	2.4%
No.	9	1.8%
none	9	1.8%
NO	5	1.0%
None that I can think of.	3	0.6%
Nope	2	0.4%
not that i can think of	2	0.4%
Other values (251)	252	50.5%
(Missing)	103	20.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	204	4.2%
of	151	3.1%
i	134	2.8%
is	132	2.7%
no	129	2.7%
to	128	2.7%
that	94	1.9%
a	81	1.7%
and	81	1.7%
not	70	1.5%
Other values (1024)	3621	75.0%

Most occurring characters

Value	Count	Frequency (%)
	4517	16.7%
e	2626	9.7%
t	2117	7.8%
o	1785	6.6%
a	1776	6.6%
n	1636	6.0%
i	1575	5.8%
s	1379	5.1%
r	1250	4.6%
h	1094	4.0%
Other values (60)	7340	27.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	21431	79.1%
Space Separator	4517	16.7%
Other Punctuation	561	2.1%
Uppercase Letter	534	2.0%
Dash Punctuation	19	0.1%
Control	12	< 0.1%
Open Punctuation	6	< 0.1%
Close Punctuation	5	< 0.1%
Decimal Number	5	< 0.1%
Final Punctuation	4	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2626	12.3%
t	2117	9.9%
o	1785	8.3%
a	1776	8.3%
n	1636	7.6%
i	1575	7.3%
s	1379	6.4%
r	1250	5.8%
h	1094	5.1%
l	816	3.8%
Other values (16)	5377	25.1%

Uppercase Letter

Value	Count	Frequency (%)
I	192	36.0%
N	130	24.3%
T	40	7.5%
A	30	5.6%
W	28	5.2%
H	18	3.4%
O	13	2.4%
S	11	2.1%
C	9	1.7%
M	9	1.7%
Other values (13)	54	10.1%

Other Punctuation

Value	Count	Frequency (%)
.	302	53.8%
,	129	23.0%
'	87	15.5%
/	16	2.9%
?	14	2.5%
"	10	1.8%
:	1	0.2%
…	1	0.2%
!	1	0.2%

Decimal Number

Value	Count	Frequency (%)
1	1	20.0%
0	1	20.0%
3	1	20.0%
4	1	20.0%
2	1	20.0%

Space Separator

Value	Count	Frequency (%)
	4517	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	19	100.0%

Control

Value	Count	Frequency (%)
	12	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	6	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	5	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	4	100.0%

Math Symbol

Value	Count	Frequency (%)
+	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	21965	81.1%
Common	5130	18.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2626	12.0%
t	2117	9.6%
o	1785	8.1%
a	1776	8.1%
n	1636	7.4%
i	1575	7.2%
s	1379	6.3%
r	1250	5.7%
h	1094	5.0%
l	816	3.7%
Other values (39)	5911	26.9%

Common

Value	Count	Frequency (%)
	4517	88.1%
.	302	5.9%
,	129	2.5%
'	87	1.7%
-	19	0.4%
/	16	0.3%
?	14	0.3%
	12	0.2%
"	10	0.2%
(	6	0.1%
Other values (11)	18	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	27090	> 99.9%
Punctuation	5	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	4517	16.7%
e	2626	9.7%
t	2117	7.8%
o	1785	6.6%
a	1776	6.6%
n	1636	6.0%
i	1575	5.8%
s	1379	5.1%
r	1250	4.6%
h	1094	4.0%
Other values (58)	7335	27.1%

Punctuation

Value	Count	Frequency (%)
’	4	80.0%
…	1	20.0%

rank_sci_repro
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.521042084

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	5
median	6
Q3	7
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.679386636
Coefficient of variation (CV)	0.3041792855
Kurtosis	0.1087548301
Mean	5.521042084
Median Absolute Deviation (MAD)	1
Skewness	-1.025007039
Sum	2755
Variance	2.820339474
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
7	203	40.7%
6	102	20.4%
5	70	14.0%
4	49	9.8%
3	42	8.4%
2	17	3.4%
1	16	3.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	16	3.2%
2	17	3.4%
3	42	8.4%
4	49	9.8%
5	70	14.0%
6	102	20.4%
7	203	40.7%

Value	Count	Frequency (%)
7	203	40.7%
6	102	20.4%
5	70	14.0%
4	49	9.8%
3	42	8.4%
2	17	3.4%
1	16	3.2%

rank_resp
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.527054108

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	5
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.057596743
Coefficient of variation (CV)	0.5833754402
Kurtosis	-1.187605078
Mean	3.527054108
Median Absolute Deviation (MAD)	2
Skewness	0.3541560179
Sum	1760
Variance	4.233704357
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	106	21.2%
2	93	18.6%
3	71	14.2%
4	70	14.0%
7	62	12.4%
6	56	11.2%
5	41	8.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	106	21.2%
2	93	18.6%
3	71	14.2%
4	70	14.0%
5	41	8.2%
6	56	11.2%
7	62	12.4%

Value	Count	Frequency (%)
7	62	12.4%
6	56	11.2%
5	41	8.2%
4	70	14.0%
3	71	14.2%
2	93	18.6%
1	106	21.2%

rank_just
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.651302605

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	4
median	5
Q3	6
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.671474984
Coefficient of variation (CV)	0.3593563192
Kurtosis	-0.5866613384
Mean	4.651302605
Median Absolute Deviation (MAD)	1
Skewness	-0.4219703321
Sum	2321
Variance	2.793828621
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
5	115	23.0%
6	96	19.2%
4	91	18.2%
7	75	15.0%
3	63	12.6%
2	33	6.6%
1	26	5.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	26	5.2%
2	33	6.6%
3	63	12.6%
4	91	18.2%
5	115	23.0%
6	96	19.2%
7	75	15.0%

Value	Count	Frequency (%)
7	75	15.0%
6	96	19.2%
5	115	23.0%
4	91	18.2%
3	63	12.6%
2	33	6.6%
1	26	5.2%

rank_anony
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.054108216

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	4
95-th percentile	6
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.587816994
Coefficient of variation (CV)	0.5198954594
Kurtosis	-0.4222403
Mean	3.054108216
Median Absolute Deviation (MAD)	1
Skewness	0.6240587048
Sum	1524
Variance	2.521162808
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
2	141	28.3%
3	105	21.0%
1	81	16.2%
4	74	14.8%
5	51	10.2%
6	34	6.8%
7	13	2.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	81	16.2%
2	141	28.3%
3	105	21.0%
4	74	14.8%
5	51	10.2%
6	34	6.8%
7	13	2.6%

Value	Count	Frequency (%)
7	13	2.6%
6	34	6.8%
5	51	10.2%
4	74	14.8%
3	105	21.0%
2	141	28.3%
1	81	16.2%

rank_harms
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.69739479

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	2
Q3	4
95-th percentile	6
Maximum	7
Range	6
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	1.808784129
Coefficient of variation (CV)	0.6705670732
Kurtosis	-0.4651366385
Mean	2.69739479
Median Absolute Deviation (MAD)	1
Skewness	0.8169441724
Sum	1346
Variance	3.271700027
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	189	37.9%
2	91	18.2%
3	66	13.2%
4	58	11.6%
5	45	9.0%
6	30	6.0%
7	20	4.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	189	37.9%
2	91	18.2%
3	66	13.2%
4	58	11.6%
5	45	9.0%
6	30	6.0%
7	20	4.0%

Value	Count	Frequency (%)
7	20	4.0%
6	30	6.0%
5	45	9.0%
4	58	11.6%
3	66	13.2%
2	91	18.2%
1	189	37.9%

rank_balance
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.975951904

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	4
median	5
Q3	6
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.616749319
Coefficient of variation (CV)	0.3249125695
Kurtosis	-0.4443151664
Mean	4.975951904
Median Absolute Deviation (MAD)	1
Skewness	-0.5992454593
Sum	2483
Variance	2.613878359
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
6	122	24.4%
5	111	22.2%
7	97	19.4%
4	68	13.6%
3	58	11.6%
2	28	5.6%
1	15	3.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	15	3.0%
2	28	5.6%
3	58	11.6%
4	68	13.6%
5	111	22.2%
6	122	24.4%
7	97	19.4%

Value	Count	Frequency (%)
7	97	19.4%
6	122	24.4%
5	111	22.2%
4	68	13.6%
3	58	11.6%
2	28	5.6%
1	15	3.0%

rank_pub_interst
Real number (ℝ_≥0)

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.573146293

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	5
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	1.760841438
Coefficient of variation (CV)	0.4927985854
Kurtosis	-0.9434350009
Mean	3.573146293
Median Absolute Deviation (MAD)	1
Skewness	0.2559463962
Sum	1783
Variance	3.100562571
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
2	96	19.2%
3	94	18.8%
4	89	17.8%
1	66	13.2%
5	66	13.2%
6	59	11.8%
7	29	5.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	66	13.2%
2	96	19.2%
3	94	18.8%
4	89	17.8%
5	66	13.2%
6	59	11.8%
7	29	5.8%

Value	Count	Frequency (%)
7	29	5.8%
6	59	11.8%
5	66	13.2%
4	89	17.8%
3	94	18.8%
2	96	19.2%
1	66	13.2%

rank_add_fac_1
Categorical

HIGH CARDINALITY
MISSING

Distinct	118
Distinct (%)	79.7%
Missing	351
Missing (%)	70.3%
Memory size	4.0 KiB

na	11
none	10
Na	5
None	5
No	3
Other values (113)	114

Length

Max length	338
Median length	134
Mean length	50.28378378
Min length	1

Characters and Unicode

Total characters	7442
Distinct characters	63
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	112 ?
Unique (%)	75.7%

Sample

1st row	Na
2nd row	Offer results to participants
3rd row	A full disclosure of any political organizations of which a researcher belongs to or has donated to within a previous time frame (such as 4 yrs).
4th row	Increase your visibility
5th row	The researchers should not intrude into the user's personal lives

Common Values

Value	Count	Frequency (%)
na	11	2.2%
none	10	2.0%
Na	5	1.0%
None	5	1.0%
No	3	0.6%
No additional factors	2	0.4%
Avoid putting out false information	1	0.2%
inclusion of all sides (far right, far left, moderate) when it comes to research on things like hate speech and false information	1	0.2%
Understanding the limits of the Internet.	1	0.2%
The social media service (twittet, facebook, etc) knows the data is being collected.	1	0.2%
Other values (108)	108	21.6%
(Missing)	351	70.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	67	5.4%
of	42	3.4%
to	37	3.0%
be	26	2.1%
and	21	1.7%
for	21	1.7%
study	20	1.6%
is	19	1.5%
participants	18	1.5%
or	17	1.4%
Other values (489)	945	76.6%

Most occurring characters

Value	Count	Frequency (%)
	1105	14.8%
e	694	9.3%
t	621	8.3%
a	517	6.9%
o	482	6.5%
i	478	6.4%
n	453	6.1%
s	402	5.4%
r	385	5.2%
h	254	3.4%
Other values (53)	2051	27.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6025	81.0%
Space Separator	1105	14.8%
Uppercase Letter	135	1.8%
Other Punctuation	129	1.7%
Decimal Number	12	0.2%
Dash Punctuation	11	0.1%
Close Punctuation	11	0.1%
Open Punctuation	11	0.1%
Control	2	< 0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	694	11.5%
t	621	10.3%
a	517	8.6%
o	482	8.0%
i	478	7.9%
n	453	7.5%
s	402	6.7%
r	385	6.4%
h	254	4.2%
l	224	3.7%
Other values (16)	1515	25.1%

Uppercase Letter

Value	Count	Frequency (%)
I	24	17.8%
N	20	14.8%
T	11	8.1%
A	11	8.1%
R	10	7.4%
D	9	6.7%
P	7	5.2%
M	6	4.4%
C	6	4.4%
H	6	4.4%
Other values (9)	25	18.5%

Other Punctuation

Value	Count	Frequency (%)
.	53	41.1%
,	36	27.9%
'	21	16.3%
"	8	6.2%
/	6	4.7%
?	4	3.1%
!	1	0.8%

Decimal Number

Value	Count	Frequency (%)
2	5	41.7%
4	3	25.0%
1	2	16.7%
5	1	8.3%
6	1	8.3%

Space Separator

Value	Count	Frequency (%)
	1105	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	11	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	11	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	11	100.0%

Control

Value	Count	Frequency (%)
	2	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6160	82.8%
Common	1282	17.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	694	11.3%
t	621	10.1%
a	517	8.4%
o	482	7.8%
i	478	7.8%
n	453	7.4%
s	402	6.5%
r	385	6.2%
h	254	4.1%
l	224	3.6%
Other values (35)	1650	26.8%

Common

Value	Count	Frequency (%)
	1105	86.2%
.	53	4.1%
,	36	2.8%
'	21	1.6%
-	11	0.9%
)	11	0.9%
(	11	0.9%
"	8	0.6%
/	6	0.5%
2	5	0.4%
Other values (8)	15	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7441	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1105	14.9%
e	694	9.3%
t	621	8.3%
a	517	6.9%
o	482	6.5%
i	478	6.4%
n	453	6.1%
s	402	5.4%
r	385	5.2%
h	254	3.4%
Other values (52)	2050	27.6%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

rank_add_fac_1_pos
Categorical

HIGH CORRELATION
MISSING

Distinct	14
Distinct (%)	8.9%
Missing	342
Missing (%)	68.5%
Memory size	4.0 KiB

8	44
1	42
0	12
3	12
4	10
Other values (9)	37

Length

Max length	4
Median length	1
Mean length	1.121019108
Min length	1

Characters and Unicode

Total characters	176
Distinct characters	15
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.6%

Sample

1st row	8
2nd row	8
3rd row	6
4th row	8
5th row	8

Common Values

Value	Count	Frequency (%)
8	44	8.8%
1	42	8.4%
0	12	2.4%
3	12	2.4%
4	10	2.0%
10	7	1.4%
6	6	1.2%
7	6	1.2%
5	5	1.0%
na	4	0.8%
Other values (4)	9	1.8%
(Missing)	342	68.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
8	44	28.0%
1	42	26.8%
0	12	7.6%
3	12	7.6%
4	10	6.4%
10	7	4.5%
6	6	3.8%
7	6	3.8%
na	6	3.8%
5	5	3.2%
Other values (3)	7	4.5%

Most occurring characters

Value	Count	Frequency (%)
1	49	27.8%
8	44	25.0%
0	19	10.8%
3	12	6.8%
4	10	5.7%
n	8	4.5%
6	6	3.4%
7	6	3.4%
a	6	3.4%
5	5	2.8%
Other values (5)	11	6.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	156	88.6%
Lowercase Letter	18	10.2%
Uppercase Letter	2	1.1%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	49	31.4%
8	44	28.2%
0	19	12.2%
3	12	7.7%
4	10	6.4%
6	6	3.8%
7	6	3.8%
5	5	3.2%
2	4	2.6%
9	1	0.6%

Lowercase Letter

Value	Count	Frequency (%)
n	8	44.4%
a	6	33.3%
o	2	11.1%
e	2	11.1%

Uppercase Letter

Value	Count	Frequency (%)
N	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	156	88.6%
Latin	20	11.4%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	49	31.4%
8	44	28.2%
0	19	12.2%
3	12	7.7%
4	10	6.4%
6	6	3.8%
7	6	3.8%
5	5	3.2%
2	4	2.6%
9	1	0.6%

Latin

Value	Count	Frequency (%)
n	8	40.0%
a	6	30.0%
o	2	10.0%
e	2	10.0%
N	2	10.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	176	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	49	27.8%
8	44	25.0%
0	19	10.8%
3	12	6.8%
4	10	5.7%
n	8	4.5%
6	6	3.4%
7	6	3.4%
a	6	3.4%
5	5	2.8%
Other values (5)	11	6.2%

rank_add_fac_2
Categorical

HIGH CORRELATION
MISSING

Distinct	47
Distinct (%)	69.1%
Missing	431
Missing (%)	86.4%
Memory size	4.0 KiB

na	9
none	7
Na	4
No	3
None	3
Other values (42)	42

Length

Max length	134
Median length	74
Mean length	22.5
Min length	2

Characters and Unicode

Total characters	1530
Distinct characters	48
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	42 ?
Unique (%)	61.8%

Sample

1st row	Na
2nd row	content-sharing
3rd row	follow up after
4th row	na
5th row	na

Common Values

Value	Count	Frequency (%)
na	9	1.8%
none	7	1.4%
Na	4	0.8%
No	3	0.6%
None	3	0.6%
Interact as a researcher. It will carry more weight if people know who is suggesting or informing. And this does matter.	1	0.2%
At the conclusion, the user should have the option to have their data dismissed.	1	0.2%
Information about who the researchers are	1	0.2%
Data Retention Policies	1	0.2%
Location (urban, rural)	1	0.2%
Other values (37)	37	7.4%
(Missing)	431	86.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	15	5.7%
na	13	5.0%
none	11	4.2%
of	7	2.7%
to	7	2.7%
no	6	2.3%
study	5	1.9%
is	4	1.5%
and	4	1.5%
be	4	1.5%
Other values (154)	186	71.0%

Most occurring characters

Value	Count	Frequency (%)
	198	12.9%
e	151	9.9%
a	116	7.6%
t	113	7.4%
n	108	7.1%
i	103	6.7%
o	98	6.4%
s	93	6.1%
r	80	5.2%
h	56	3.7%
Other values (38)	414	27.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1269	82.9%
Space Separator	198	12.9%
Uppercase Letter	48	3.1%
Other Punctuation	12	0.8%
Open Punctuation	1	0.1%
Close Punctuation	1	0.1%
Dash Punctuation	1	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	151	11.9%
a	116	9.1%
t	113	8.9%
n	108	8.5%
i	103	8.1%
o	98	7.7%
s	93	7.3%
r	80	6.3%
h	56	4.4%
u	44	3.5%
Other values (15)	307	24.2%

Uppercase Letter

Value	Count	Frequency (%)
N	12	25.0%
P	9	18.8%
I	4	8.3%
A	4	8.3%
M	4	8.3%
L	2	4.2%
W	2	4.2%
R	2	4.2%
C	1	2.1%
D	1	2.1%
Other values (7)	7	14.6%

Other Punctuation

Value	Count	Frequency (%)
.	8	66.7%
,	4	33.3%

Space Separator

Value	Count	Frequency (%)
	198	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1317	86.1%
Common	213	13.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	151	11.5%
a	116	8.8%
t	113	8.6%
n	108	8.2%
i	103	7.8%
o	98	7.4%
s	93	7.1%
r	80	6.1%
h	56	4.3%
u	44	3.3%
Other values (32)	355	27.0%

Common

Value	Count	Frequency (%)
	198	93.0%
.	8	3.8%
,	4	1.9%
(	1	0.5%
)	1	0.5%
-	1	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1530	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	198	12.9%
e	151	9.9%
a	116	7.6%
t	113	7.4%
n	108	7.1%
i	103	6.7%
o	98	6.4%
s	93	6.1%
r	80	5.2%
h	56	3.7%
Other values (38)	414	27.1%

rank_add_fac_2_pos
Categorical

HIGH CORRELATION
MISSING

Distinct	15
Distinct (%)	15.5%
Missing	402
Missing (%)	80.6%
Memory size	4.0 KiB

9	29
2	20
0	11
1	8
10	7
Other values (10)	22

Length

Max length	4
Median length	1
Mean length	1.226804124
Min length	1

Characters and Unicode

Total characters	119
Distinct characters	16
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	5.2%

Sample

1st row	9
2nd row	5
3rd row	3
4th row	na
5th row	3

Common Values

Value	Count	Frequency (%)
9	29	5.8%
2	20	4.0%
0	11	2.2%
1	8	1.6%
10	7	1.4%
na	5	1.0%
5	4	0.8%
8	4	0.8%
3	2	0.4%
Na	2	0.4%
Other values (5)	5	1.0%
(Missing)	402	80.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
9	29	29.9%
2	20	20.6%
0	11	11.3%
1	8	8.2%
na	8	8.2%
10	7	7.2%
5	4	4.1%
8	4	4.1%
3	2	2.1%
none	2	2.1%
Other values (2)	2	2.1%

Most occurring characters

Value	Count	Frequency (%)
9	29	24.4%
2	20	16.8%
0	18	15.1%
1	15	12.6%
n	8	6.7%
a	7	5.9%
5	4	3.4%
8	4	3.4%
N	4	3.4%
3	2	1.7%
Other values (6)	8	6.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	94	79.0%
Lowercase Letter	19	16.0%
Uppercase Letter	5	4.2%
Space Separator	1	0.8%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
9	29	30.9%
2	20	21.3%
0	18	19.1%
1	15	16.0%
5	4	4.3%
8	4	4.3%
3	2	2.1%
7	1	1.1%
6	1	1.1%

Lowercase Letter

Value	Count	Frequency (%)
n	8	42.1%
a	7	36.8%
o	2	10.5%
e	2	10.5%

Uppercase Letter

Value	Count	Frequency (%)
N	4	80.0%
A	1	20.0%

Space Separator

Value	Count	Frequency (%)
	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	95	79.8%
Latin	24	20.2%

Most frequent character per script

Common

Value	Count	Frequency (%)
9	29	30.5%
2	20	21.1%
0	18	18.9%
1	15	15.8%
5	4	4.2%
8	4	4.2%
3	2	2.1%
7	1	1.1%
	1	1.1%
6	1	1.1%

Latin

Value	Count	Frequency (%)
n	8	33.3%
a	7	29.2%
N	4	16.7%
o	2	8.3%
e	2	8.3%
A	1	4.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	119	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
9	29	24.4%
2	20	16.8%
0	18	15.1%
1	15	12.6%
n	8	6.7%
a	7	5.9%
5	4	3.4%
8	4	3.4%
N	4	3.4%
3	2	1.7%
Other values (6)	8	6.7%

rank_add_fac_3
Categorical

HIGH CORRELATION
MISSING

Distinct	41
Distinct (%)	64.1%
Missing	435
Missing (%)	87.2%
Memory size	4.0 KiB

na	9
none	8
Na	6
No	3
None	2
Other values (36)	36

Length

Max length	95
Median length	76
Mean length	20.296875
Min length	2

Characters and Unicode

Total characters	1299
Distinct characters	52
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	36 ?
Unique (%)	56.2%

Sample

1st row	Na
2nd row	communication
3rd row	give results after entire experiment is done
4th row	na
5th row	na

Common Values

Value	Count	Frequency (%)
na	9	1.8%
none	8	1.6%
Na	6	1.2%
No	3	0.6%
None	2	0.4%
Adherence to Regulations (like GDPR)	1	0.2%
ethical application in the real world	1	0.2%
No one should die	1	0.2%
Income	1	0.2%
N/a	1	0.2%
Other values (31)	31	6.2%
(Missing)	435	87.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
na	15	6.6%
the	13	5.8%
none	11	4.9%
of	9	4.0%
no	7	3.1%
study	6	2.7%
be	5	2.2%
and	5	2.2%
to	5	2.2%
data	5	2.2%
Other values (116)	145	64.2%

Most occurring characters

Value	Count	Frequency (%)
	165	12.7%
e	127	9.8%
n	106	8.2%
t	106	8.2%
a	95	7.3%
o	92	7.1%
i	89	6.9%
s	68	5.2%
r	54	4.2%
c	43	3.3%
Other values (42)	354	27.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1063	81.8%
Space Separator	165	12.7%
Uppercase Letter	47	3.6%
Other Punctuation	18	1.4%
Control	3	0.2%
Final Punctuation	1	0.1%
Open Punctuation	1	0.1%
Close Punctuation	1	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	127	11.9%
n	106	10.0%
t	106	10.0%
a	95	8.9%
o	92	8.7%
i	89	8.4%
s	68	6.4%
r	54	5.1%
c	43	4.0%
d	42	4.0%
Other values (14)	241	22.7%

Uppercase Letter

Value	Count	Frequency (%)
N	14	29.8%
R	4	8.5%
C	4	8.5%
I	3	6.4%
P	3	6.4%
A	3	6.4%
U	2	4.3%
D	2	4.3%
H	2	4.3%
W	2	4.3%
Other values (7)	8	17.0%

Other Punctuation

Value	Count	Frequency (%)
.	10	55.6%
,	3	16.7%
/	2	11.1%
?	1	5.6%
'	1	5.6%
:	1	5.6%

Space Separator

Value	Count	Frequency (%)
	165	100.0%

Control

Value	Count	Frequency (%)
	3	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1110	85.5%
Common	189	14.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	127	11.4%
n	106	9.5%
t	106	9.5%
a	95	8.6%
o	92	8.3%
i	89	8.0%
s	68	6.1%
r	54	4.9%
c	43	3.9%
d	42	3.8%
Other values (31)	288	25.9%

Common

Value	Count	Frequency (%)
	165	87.3%
.	10	5.3%
	3	1.6%
,	3	1.6%
/	2	1.1%
’	1	0.5%
?	1	0.5%
'	1	0.5%
(	1	0.5%
)	1	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1298	99.9%
Punctuation	1	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	165	12.7%
e	127	9.8%
n	106	8.2%
t	106	8.2%
a	95	7.3%
o	92	7.1%
i	89	6.9%
s	68	5.2%
r	54	4.2%
c	43	3.3%
Other values (41)	353	27.2%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

rank_add_fac_3_pos
Categorical

HIGH CORRELATION
MISSING

Distinct	13
Distinct (%)	14.3%
Missing	408
Missing (%)	81.8%
Memory size	4.0 KiB

10	31
3	15
0	11
1	7
na	5
Other values (8)	22

Length

Max length	4
Median length	1
Mean length	1.494505495
Min length	1

Characters and Unicode

Total characters	136
Distinct characters	14
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	1.1%

Sample

1st row	10
2nd row	8
3rd row	2
4th row	na
5th row	2

Common Values

Value	Count	Frequency (%)
10	31	6.2%
3	15	3.0%
0	11	2.2%
1	7	1.4%
na	5	1.0%
9	5	1.0%
8	4	0.8%
2	4	0.8%
6	2	0.4%
none	2	0.4%
Other values (3)	5	1.0%
(Missing)	408	81.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
10	31	34.1%
3	15	16.5%
0	11	12.1%
1	7	7.7%
na	7	7.7%
9	5	5.5%
8	4	4.4%
2	4	4.4%
6	2	2.2%
none	2	2.2%
Other values (2)	3	3.3%

Most occurring characters

Value	Count	Frequency (%)
0	42	30.9%
1	39	28.7%
3	15	11.0%
n	9	6.6%
a	7	5.1%
9	5	3.7%
8	4	2.9%
2	4	2.9%
6	2	1.5%
o	2	1.5%
Other values (4)	7	5.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	113	83.1%
Lowercase Letter	20	14.7%
Uppercase Letter	3	2.2%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	42	37.2%
1	39	34.5%
3	15	13.3%
9	5	4.4%
8	4	3.5%
2	4	3.5%
6	2	1.8%
4	2	1.8%

Lowercase Letter

Value	Count	Frequency (%)
n	9	45.0%
a	7	35.0%
o	2	10.0%
e	2	10.0%

Uppercase Letter

Value	Count	Frequency (%)
N	2	66.7%
O	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	113	83.1%
Latin	23	16.9%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	42	37.2%
1	39	34.5%
3	15	13.3%
9	5	4.4%
8	4	3.5%
2	4	3.5%
6	2	1.8%
4	2	1.8%

Latin

Value	Count	Frequency (%)
n	9	39.1%
a	7	30.4%
o	2	8.7%
e	2	8.7%
N	2	8.7%
O	1	4.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	136	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	42	30.9%
1	39	28.7%
3	15	11.0%
n	9	6.6%
a	7	5.1%
9	5	3.7%
8	4	2.9%
2	4	2.9%
6	2	1.5%
o	2	1.5%
Other values (4)	7	5.1%

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	df_index	lat	long	sm_use	age	gender_id	ethnic_id	edu	politic_pref	sm_res_purp	sm_aware	sm_expmt_inerct	sm_data_use	ethic_appr	study_1_ethic_acc	study_1_conc	study_1_add_info	study_2_ethic_acc	study_2_conc	study_2_add_info	study_3_ethic_acc	study_3_conc	study_3_add_info	study_4_ethic_acc	study_4_conc	study_4_add_info	design_cont	design_num_users	design_res_purp	design_len_data	design_admin_inter	design_inter_type	design_partic_aware	design_inter_impact	design_type_data	design_add_fac	rank_sci_repro	rank_resp	rank_just	rank_anony	rank_harms	rank_balance	rank_pub_interst	rank_add_fac_1	rank_add_fac_1_pos	rank_add_fac_2	rank_add_fac_2_pos	rank_add_fac_3	rank_add_fac_3_pos
0	1	47.6034	-122.3414	Facebook	29	Male	Asian - Eastern	Highschool	Slightly liberal	Extremely aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys)	Creating fake accounts ("bots"),Secretly changing the content of what users see	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Slightly important	Slightly important	Not at all important	Not at all important	No.	2	7	5	6	4	3	1	NaN	NaN	NaN	NaN	NaN	NaN
1	2	33.058	-80.0101	Twitter	33	Male	Mixed race	Highschool	Neutral/ Neither conservative or liberal	Moderately aware	… are large and can contain millions of data points	Privately messaging users,Publicly posting on users' profiles,Secretly changing the content of what users see	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	I think Ethical Approval means that the experiment is gathering data without harm or injury to people.	Completely acceptable	NaN	NaN	Completely acceptable	NaN	NaN	Completely accepatable	NaN	NaN	Neutral	NaN	NaN	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Moderately important	Not at all important	Not at all important	The only aspects of social media research that would cause concern for me is saving photographs or imaging data.	3	5	2	6	1	7	4	NaN	NaN	NaN	NaN	NaN	NaN
2	3	43.2817	-71.6595	Facebook	33	Female	Pacific Islander	Bachelor's degree	Very liberal	Extremely aware	… are large and can contain millions of data points,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots"),Secretly changing the content of what users see	Political elections (e.g. voting behavior),Presidential approval ratings,Communication (e.g. spread of opinions and hate-speech),News consumption (e.g. sharing of misinformation),Social networks	Researchers focus on ethical standards towards those they gain data from. They need approval of their approach and receive methods.	Completely acceptable	No concerns. I would have loved to partake in this study in terms of watching the results.	NaN	Completely acceptable	Going to the poster privately provided opportunity for change without the possibly of increased toxicity from users. I prefer this method over commenting the "correct information".	NaN	Somewhat acceptable	I find this is ethical as long as participants were fully aware of what was being monitored. The results are interesting! No concerns.	NaN	Somewhat unacceptable	I am uncertain how I feel completely about a researcher creating a fake account. However I do understand the desire to protect themselves and to not give away their actions as being part of a study. This misinformation needed to be corrected for the public but it opened the original poster to toxicity. The OP may not have known it was incorrect.	The researchers had a purpose in seeing the responses of those interacting with the post. I do not agree with how it was done entirely however I do not know a better way to get the results that were desired.	Extremely important	Very important	Very important	Extremely important	Moderately important	Very important	Moderately important	Extremely important	Not at all important	None that I can think of, other than what has been asked already.	7	5	6	3	2	4	1	Na	NaN	Na	NaN	Na	NaN
3	4	35.8437	-86.3881	Facebook	73	Female	White / Caucasian	Highschool	Slightly conservative	Moderately aware	… are large and can contain millions of data points,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	I would think that using "ethical approval" means that the things others collect on social media sites would need to be honest and moral. Hopefully, there would be no under-handedness used in collecting information.	Neutral	I feel if people know they are being judged they will act, speak, or write differently than if they don't know they are being analyzed.	NaN	Somewhat acceptable	I feel as though, in the above case, users had a choice to respond or not so I think it was honest.	NaN	Somewhat acceptable	As long as the Facebook users were informed that they would be in a study I feel it is fair. It was up to the users whether they wanted to participate or not. Also, they were encouraged, but not actually made to Like the Facebook study.	NaN	Somewhat unacceptable	Users were not aware of what was going on so they were possibly more honest in their opinions because they had no idea they were being analyzed.	NaN	Moderately important	Moderately important	Extremely important	Very important	Moderately important	Very important	Extremely important	Very important	Very important	Reducing any type of hate is always a good thing.	7	2	6	3	4	5	1	Offer results to participants	8	NaN	NaN	NaN	NaN
4	5	34.7456	-92.3419	Twitter	27	Female	Native-American	Highschool	Very liberal	Extremely aware	… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	A set of rules of what to do and what to not do.	Completely acceptable	NaN	NaN	Completely acceptable	NaN	NaN	Completely unacceptable	The web extension being used was invasive, even if it was used with consent. The people participating in the study are not educated enough on exactly how much information the web extension was taking.	Making the source code for the web extension publicly available to have complete transparency over what the extension was doing.	Completely acceptable	NaN	NaN	Extremely important	Not at all important	Not at all important	Not at all important	Not at all important	Extremely important	Slightly important	Not at all important	Extremely important	NaN	3	1	5	2	4	6	7	NaN	NaN	NaN	NaN	NaN	NaN
5	6	25.6639	-80.4372	Facebook	49	Female	Hispanic	Bachelor's degree	Slightly liberal	Slightly aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Publicly posting on users' profiles,Creating fake accounts ("bots"),Hacking into users' accounts	Political elections (e.g. voting behavior),Health topics (e.g. spread of diseases),Well-being and economic satisfaction,News consumption (e.g. sharing of misinformation),Social networks	is when the participants have the right to know who was access to their data and what is being done with it.	Somewhat acceptable	na	na	Somewhat unacceptable	na	na	Completely accepatable	na	na	Somewhat acceptable	na	na	Very important	Moderately important	Extremely important	Very important	Very important	Very important	Very important	Very important	Very important	no	7	2	6	4	1	3	5	NaN	NaN	NaN	NaN	NaN	NaN
6	7	44.2433	-88.3564	Facebook	53	Male	White / Caucasian	Highschool	Slightly conservative	Slightly aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect,… are unaffected by the way social media platforms work	None of the above	Political elections (e.g. voting behavior),Presidential approval ratings,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	Verification of some sort that social media users and/or the data being used is not being skewed to support a theory or the results in any way.	Completey unacceptable	Easy enough for an outside government to try copying such a study with the sole purpose of creating much more polarization, hate, etc. Not that it hasn't been tried and tested perhaps innumerable times by all types of foreign or domestic entities as far as we know. No actual study would have really been needed to know that using a type of marketing manipulation could alter the recipients mood/levels of concern/anxiety/hate/etc.	NaN	Somewhat acceptable	NaN	Concerns over the possibility of the researchers having their own political agenda. Yet fake news is a major problem. What social media really is when mass sharing news (political news), is simple propaganda from the left and right.	Neutral	The researchers seem in some ways to try manipulating political viewpoints in a segment of the population for the sake of science.	NaN	Neutral	Many of the people that have large political followings on twitter (and many who don't) often know already the news they are sharing is fake. It's political partisanship and the spreading of propaganda. Some might post fake news only to gain more followers (the masses) if they believe it serves that end.	NaN	Moderately important	Extremely important	Slightly important	Very important	Moderately important	Extremely important	Extremely important	Extremely important	Very important	NA. I have already voiced my concerns about researching this in general from this surveys other questions.	6	7	5	3	2	4	1	A full disclosure of any political organizations of which a researcher belongs to or has donated to within a previous time frame (such as 4 yrs).	8	NaN	9	NaN	10
7	8	42.0307	-87.8107	Reddit	29	Female	White / Caucasian	Highschool	Slightly liberal	Moderately aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Economic forecasting,Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	Going through a process of peer review maybe? Like earlier you mentioned creating bot accounts, so maybe making sure the researcher isn’t spreading hate or misinformation	Completely acceptable	NaN	NaN	Completely acceptable	NaN	NaN	Completely accepatable	NaN	NaN	Somewhat acceptable	NaN	NaN	Very important	Very important	Moderately important	Extremely important	Moderately important	Moderately important	Moderately important	Extremely important	Very important	The possibility of bot accounts spreading misinformation or hate speech just for the purpose of an experiment	7	6	5	4	2	1	3	NaN	NaN	NaN	NaN	NaN	NaN
8	9	33.8838	-118.1261	Facebook	23	Male	White / Caucasian	Bachelor's degree	Neutral/ Neither conservative or liberal	Moderately aware	… are unaffected by the way social media platforms work	Publicly posting on users' profiles	Social networks	Social media is a collective term for websites and applications that focus on communication, community-based input, interaction, content-sharing and collaboration.	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	No, i didn't anything like that.	5	4	7	2	6	3	1	Increase your visibility	6	content-sharing	5	communication	8
9	10	32.1453	-110.9456	Facebook	65	Male	Hispanic	Highschool	Very liberal	Moderately aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	Whether or not something goes against someone's right to privacy online.	Completely acceptable	NaN	I would be interested to know what kind of messages they sent the hate speech users that got them to change their minds.	Completely acceptable	It's perfectly within someone's right to send someone else a message on any platform, therefore I believe this study was acceptable.	NaN	Completely accepatable	People willingly consented to being part of the research study, so I believe the study was completely acceptable.	NaN	Completely acceptable	NaN	NaN	Very important	Not at all important	Slightly important	Not at all important	Not at all important	Not at all important	Moderately important	Very important	Not at all important	None.	5	3	7	4	1	6	2	The researchers should not intrude into the user's personal lives	8	NaN	NaN	NaN	NaN

Last rows

	df_index	lat	long	sm_use	age	gender_id	ethnic_id	edu	politic_pref	sm_res_purp	sm_aware	sm_expmt_inerct	sm_data_use	ethic_appr	study_1_ethic_acc	study_1_conc	study_1_add_info	study_2_ethic_acc	study_2_conc	study_2_add_info	study_3_ethic_acc	study_3_conc	study_3_add_info	study_4_ethic_acc	study_4_conc	study_4_add_info	design_cont	design_num_users	design_res_purp	design_len_data	design_admin_inter	design_inter_type	design_partic_aware	design_inter_impact	design_type_data	design_add_fac	rank_sci_repro	rank_resp	rank_just	rank_anony	rank_harms	rank_balance	rank_pub_interst	rank_add_fac_1	rank_add_fac_1_pos	rank_add_fac_2	rank_add_fac_2_pos	rank_add_fac_3	rank_add_fac_3_pos
489	490	37.1235	-76.4502	Facebook	37	Female	White / Caucasian	Master's degree or above	Slightly liberal	Very aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	None of the above	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	That the researchers would use the acquired users data in an ethical manner without manipulating it. Keeping the users data safe and secure.	Somewhat acceptable	Accounts were anonymous and operated by the human which is fine and the outcome was awesome so I would say that type of research is somewhat acceptable.	NaN	Somewhat unacceptable	Creating fake accounts and sending unsolicited private messages to users is unethical.	Not ethically acceptable to me.	Completely unacceptable	the researchers tried to bribe and manipulate the social media users	I do not approve this practice by the researchers	Somewhat unacceptable	creating fake accounts for research or any other purposes is not acceptable or ethical to me.	NaN	Very important	Very important	Extremely important	Extremely important	Extremely important	Very important	Extremely important	Extremely important	Extremely important	Privacy of users data.	7	3	1	2	6	5	4	NaN	NaN	NaN	NaN	NaN	NaN
490	491	30.4941	-90.4751	Twitter	44	Male	African-American	Highschool	Slightly liberal	Moderately aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	To be approved by the original source it came from.	Completely acceptable	This should be done more often, It's a good thing to do, completely acceptable.	Hate speech is a serious issue, we need to do better.	Somewhat acceptable	I think it's in their best concerns to reduce the amount of misinformation, and also help fact check what's posted.	It's acceptable on my behalf due to the researchers posting facts.	Somewhat acceptable	I can relate to going to another news source to see what information they're giving, and doing this study in this type of way is intriguing.	NaN	Neutral	I'm not sure if this is good, or bad	NaN	Extremely important	Extremely important	Extremely important	Extremely important	Very important	Extremely important	Very important	Extremely important	Extremely important	Who the researchers are targeting on social media sites. Race, sex, job type, political view.	2	4	5	7	3	1	6	NaN	NaN	NaN	NaN	NaN	NaN
491	492	26.6054	-81.7284	Facebook	39	Female	White / Caucasian	Highschool	Slightly conservative	Extremely aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Creating fake accounts ("bots"),Secretly changing the content of what users see	Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation)	It means that possible risks have been considered and deemed acceptable.	Completey unacceptable	Participants should have the right to accept or decline to participate in the study.	NaN	Completely unacceptable	Participants should be made aware of the study and have the option to either accept or decline being included in it.	NaN	Completely accepatable	NaN	NaN	Completely unacceptable	The participants should have been made aware that they were part of a study and either accept or decline taking part in it.	NaN	Very important	Very important	Very important	Extremely important	Moderately important	Moderately important	Extremely important	Very important	Very important	none	7	1	4	2	3	5	6	NaN	NaN	NaN	NaN	NaN	NaN
492	493	40.5662	-79.7078	Reddit	54	Male	White / Caucasian	Bachelor's degree	Neutral/ Neither conservative or liberal	Slightly aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	Assurance that the experimenters will use the data and information collected only for the purpose explained in the study. Also, that the person being polled is aware of their rights and redresses, if necessary, by a board or body overseeing the researchers. Generally, that the experiment will cause no foreseeable harm to the people being polled.	Neutral	It's concerning that the study misrepresented the nature of the anonymous accounts who replied to the message. It's understandable that they wanted sincere reactions to the messages they sent and that informing the recipients they weren't real people could have caused the messages to be disregarded or met with a level of denial, but since there were a range of responses to the original hate speech, I wonder if any of the replies were incendiary, which could cause the original user to get even more emotionally involved, stressed, or angry, which could lead to actual violence or emotional distress. I'd imagine if they were trying to measure how people reacted to different messages they would have to have them grouped into at least Empathetic, Neutral, and Contrary types of messages the researchers sent.	It's hard to judge without seeing the actual content of the messages, so I'd want to see that and who is overseeing the study and how closely it's being monitored.	Somewhat acceptable	Same as the others, that the subjects were unaware of the experiment. I do find the anonymous accounts more acceptable than the human-looking automated accounts.	NaN	Completely accepatable	NaN	It seems the study was forthcoming and transparent and that participants had to opt in to join it, so I can't see any issues, as long as all other processes are in place (eg. the study is being overseen, etc.)	Somewhat acceptable	While most of the study seems innocuous, for instance, the bot is just replying with a Tweet about fact-checking that the user can choose not to click, it's always concerning when the subjects don't know they're part of an experiment and that the automated accounts were apparently made to look like a human user.	I'd want to make sure the study is only using publicly-made Twitter statements and not going any further by looking into other social media sites the user might have linked or any other biographical information that could be discerned.	Slightly important	Not at all important	Slightly important	Not at all important	Very important	Slightly important	Extremely important	Not at all important	Extremely important	NaN	7	2	5	6	1	3	4	NaN	NaN	NaN	NaN	NaN	NaN
493	494	34.0264	-117.936	Facebook	32	Male	White / Caucasian	Master's degree or above	Slightly conservative	Very aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	Ethical approval means getting approval from the University or the government or both.	Completely acceptable	NaN	NaN	Completely acceptable	NaN	Some people may not want to be contacted privately.	Completely unacceptable	Choosing between the results of their own data or money is completely unacceptable. Why should participants have to pay to view their own data? They created it, so they should have access to it if they want it.	NaN	Completely acceptable	NaN	NaN	Very important	Very important	Very important	Slightly important	Very important	Extremely important	Very important	Extremely important	Extremely important	NaN	7	2	5	1	4	3	6	None	NaN	NaN	NaN	NaN	NaN
494	495	37.2697	-81.2212	Facebook	35	Female	White / Caucasian	Bachelor's degree	Neutral/ Neither conservative or liberal	Moderately aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Creating fake accounts ("bots"),Secretly changing the content of what users see	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	Approval to do any type of thing that might be deceptive.	Completely acceptable	NaN	NaN	Somewhat unacceptable	It seems a little too deceptive to me.	NaN	Completely accepatable	They weren't really deceptive.	NaN	Completely acceptable	NaN	NaN	Moderately important	Moderately important	Very important	Very important	Very important	Very important	Extremely important	Very important	Very important	No.	6	5	4	1	3	7	2	NaN	NaN	NaN	NaN	NaN	NaN
495	496	40.5828	-73.9532	Facebook	39	Male	White / Caucasian	Master's degree or above	Very conservative	Moderately aware	… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect,… are always representative of people’s offline behavior,… are unaffected by the way social media platforms work	Publicly posting on users' profiles	Health topics (e.g. spread of diseases),Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	It has to with researchers taking a mental note of the standards meant to be followed while conducting research experiment.	Completely acceptable	NaN	NaN	Somewhat acceptable	NaN	NaN	Somewhat acceptable	NaN	NaN	Completely acceptable	NaN	NaN	Extremely important	Slightly important	Extremely important	Moderately important	Slightly important	Extremely important	Extremely important	Slightly important	Very important	I can't think of any other aspects.	7	3	2	4	1	5	6	NaN	NaN	NaN	NaN	NaN	NaN
496	497	40.2602	-76.8591	Facebook	37	Female	African-American	Highschool	Very liberal	Not at all aware	… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Communication (e.g. spread of opinions and hate-speech),News consumption (e.g. sharing of misinformation),Social networks	I think ethical approval means that institutions have to deem the experiments as tests that most would approve of.	Completely acceptable	NaN	NaN	Completely acceptable	NaN	NaN	Completely accepatable	NaN	NaN	Completely acceptable	NaN	NaN	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Slightly important	Not at all important	Not at all important	No	7	4	6	3	1	2	5	NaN	NaN	NaN	NaN	NaN	NaN
497	498	40.8275	-73.1225	Reddit	23	Female	African-American	Highschool	Slightly liberal	Slightly aware	… are large and can contain millions of data points,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Publicly posting on users' profiles	Political elections (e.g. voting behavior),Economic forecasting,Presidential approval ratings,Well-being and economic satisfaction,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	I think ethical approval means that the experiment has to be deemed as appropriate, safe, and not have long-term consequences.	Somewhat unacceptable	It is unacceptable that the users were never made aware that it was a study and the researcher analyzed the user's behaviors for weeks.	NaN	Somewhat unacceptable	It is good that the researchers only examined data that was collected during the experiment period but unacceptable that the users were messaged privately and were not made aware that it was a experiment.	NaN	Somewhat acceptable	It is good that users were made aware that it was a study and what the users had to do was related to the research topic.	NaN	Neutral	It is good that the researchers only analyzed the users' behaviors for a short period after the experiment but it is not appropriate that the researchers never told the users that it was an experiment.	NaN	Slightly important	Not at all important	Not at all important	Extremely important	Not at all important	Moderately important	Very important	Very important	Moderately important	NaN	6	2	7	3	4	5	1	Long-term effects of the experiment	4	NaN	NaN	NaN	NaN
498	499	39.0518	-94.4046	Twitter	55	Male	White / Caucasian	Vocational training	Very liberal	Moderately aware	… are large and can contain millions of data points,… reflect events in real-time and can be collected continuously over time,… are naturalistic in that they do not require researchers to directly interact with research volunteers,… often capture social relationships not found using traditional methods (e.g. surveys),… are readily accessible to researchers and easy to collect	Privately messaging users,Publicly posting on users' profiles,Creating fake accounts ("bots")	Political elections (e.g. voting behavior),Presidential approval ratings,Communication (e.g. spread of opinions and hate-speech),Public sentiment (e.g. environment-related concerns),News consumption (e.g. sharing of misinformation),Social networks	I think ethical approval is that an academic experiment is run in an ethical way. Meaning that the researchers adhere to ethical standards.	Somewhat unacceptable	The fact that participants were not aware they were part of a research study is a concern.	NaN	Somewhat unacceptable	I find it somewhat unacceptable that researchers sent unsolicited private messages.	If the researchers had contacted the twitter users instead of sending unsolicited private messages I would probably find it a little more ethical.	Completely accepatable	NaN	NaN	Somewhat unacceptable	The users were not informed that they are part of an academic research and deceived by human looking automatic accounts.	NaN	Moderately important	Not at all important	Moderately important	Moderately important	Moderately important	Very important	Extremely important	Slightly important	Moderately important	NaN	7	1	3	2	5	6	4	NaN	NaN	NaN	NaN	NaN	NaN

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